runf2.log

logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00000_0_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.02/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00000_0_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.02/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00000_0_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.02/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
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VL-std-all 0.0 0.0
*** budget = 10
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RT-VL10-all -7.965720295135137 1.1322092380714586 -6.283774521389187 -9.03894524166936
*** budget = 20
VL20-1 0.16625862147602408 0.057849002654906105
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VL20-2 0.03897088875480043 0.030288119396349997
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VL20-3 0.009123918362552033 0.009504128796656118
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VL20-4 0.0071471558645129505 0.007877826990310555
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VL20-5 0.056287537980666304 0.03506323017317606
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RT-VL20-all -8.099839085537528 1.4305545942975373 -5.99534529821124 -9.594690263886982
*** budget = 30
VL30-1 0.14117332768065696 0.04693106763712281
RT-VL30-1 -9.703921338536935 5.355629411754478 -4.778312657592606 -24.411108030234452
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VL30-3 0.00553664944752148 0.007613844079327908
RT-VL30-3 -9.541661810462951 3.4526100131933326 -6.79919218880113 -20.000024138165212
VL30-4 0.004421235856541788 0.005084326224787843
RT-VL30-4 -6.837011633819187 0.5978261638914596 -5.794921297681345 -9.75122547791558
VL30-5 0.04241729535328449 0.02368000020845762
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VL30-all 0.04391751743037118 0.05062337236170858
RT-VL30-all -8.189039441149285 1.4586153836836304 -6.101446687588188 -9.703921338536935
*** budget = 40
VL40-1 0.13011229238242353 0.04441543200178986
RT-VL40-1 -9.802453957452078 5.39582080889928 -4.778312657592606 -24.411108030234452
VL40-2 0.021210118355997527 0.01477385863700572
RT-VL40-2 -8.717246296015103 4.360008432228834 -5.0620309242484245 -24.411108030234452
VL40-3 0.003991220405136523 0.006514085583497908
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VL40-4 0.003157154437240826 0.0030202714201883075
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VL40-5 0.03565016261828896 0.021682064831283116
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VL40-all 0.03882418963981747 0.04720025593768651
RT-VL40-all -8.159718389387335 1.3906757486711774 -6.19582719117515 -9.802453957452078
*** budget = 50
VL50-1 0.12353892611699914 0.04308369390291954
RT-VL50-1 -9.84416677061005 5.403675792233063 -4.778312657592606 -24.411108030234452
VL50-2 0.018405468548843736 0.013158880766658465
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VL50-4 0.002227637474622653 0.0020984578869545803
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VL50-all 0.03534588825761709 0.04527522832167161
RT-VL50-all -8.20747630100529 1.3736178516342668 -6.353321202653738 -9.84416677061005
logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00002_2_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s2/08.10_20.42.59/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00002_2_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s2/08.10_20.42.59/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00002_2_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s2/08.10_20.42.59/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
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LR -0.1995883828818102 0.020033190310997928 -0.0027024071615964207 -0.3256607193277654
VL-std-all 0.0 0.0
*** budget = 10
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*** budget = 20
VL20-1 0.27903623455276483 0.05192391198120342
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VL20-4 0.13835505682256358 0.03614466208611046
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*** budget = 30
VL30-1 0.2638209391091466 0.056367781775010034
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VL30-3 0.0008750718866311624 7.764766979939884e-05
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*** budget = 40
VL40-1 0.2531989267296872 0.057820110806827384
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VL40-3 0.0008662058807030298 8.857303965757831e-05
RT-VL40-3 -20.112487995247747 0.5509497036014824 -19.891286743306683 -23.316495450033056
VL40-4 0.1166898707467698 0.04266750610917039
RT-VL40-4 -17.305325353992508 6.199102722024616 -5.869600453486886 -28.015379042061575
VL40-5 0.2729290293589645 0.051808072155949514
RT-VL40-5 -17.305325353992508 6.199102722024616 -5.869600453486886 -28.015379042061575
VL40-all 0.14865669714115687 0.10162992750432216
RT-VL40-all -18.88634401806012 1.2943346258348591 -17.305325353992508 -20.112487995247747
*** budget = 50
VL50-1 0.24390675489920163 0.06018213856470338
RT-VL50-1 -19.50338474256835 3.494665772034287 -10.269329572270978 -24.024231986772108
VL50-2 0.0918007372865012 0.04727428427370028
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VL50-3 0.0008582492087162441 9.66493394263756e-05
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VL50-4 0.10988828289197097 0.04343228961714025
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logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00004_4_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s3/08.10_20.42.58/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00004_4_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s3/08.10_20.42.58/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00004_4_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s3/08.10_20.42.58/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.921043861029027 0.9823662036768975 -5.982822397202772 -25.47683277150537
LR -0.19871971756361032 0.017529742631740626 -0.005297915005370912 -0.29957495652073174
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.31776599713070336 0.007052717725108231
RT-VL10-1 -19.97879777445618 0.72552678331747 -13.982398510886767 -22.94436624274295
VL10-2 0.15776599713070336 0.0070527177251082296
RT-VL10-2 -19.97879777445618 0.72552678331747 -13.982398510886767 -22.94436624274295
VL10-3 0.0009003059035035295 3.229630875266894e-17
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.15256062784113297 0.025663940985483677
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VL10-5 0.2941969876054463 0.075818118463925
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VL10-all 0.1846379831222979 0.11422507426995628
RT-VL10-all -19.80816023067047 0.26878371648649135 -19.29253377628436 -20.00002413816521
*** budget = 20
VL20-1 0.3164323001649102 0.009794087983420394
RT-VL20-1 -19.957571410747153 1.025610603294142 -13.982398510886767 -22.94436624274295
VL20-2 0.15643230016491017 0.009794087983420394
RT-VL20-2 -19.957571410747153 1.025610603294142 -13.982398510886767 -22.94436624274295
VL20-3 0.0009003059035035207 4.2025882221200105e-17
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.14534632755607257 0.033458396795477574
RT-VL20-4 -19.638874590843 2.49641701596711 -8.575292844546164 -25.47683277150537
VL20-5 0.2699132390852127 0.10051662851803476
RT-VL20-5 -18.609884126949595 3.79185855790224 -5.982822397202772 -25.47683277150537
VL20-all 0.17780489457492182 0.11000644032671292
RT-VL20-all -19.632785135490423 0.5276741966433809 -18.609884126949595 -20.00002413816521
*** budget = 30
VL30-1 0.31509860319911703 0.011770726245681356
RT-VL30-1 -19.93634504703813 1.2555731714381033 -13.982398510886767 -22.94436624274295
VL30-2 0.15509860319911695 0.011770726245681356
RT-VL30-2 -19.93634504703813 1.2555731714381033 -13.982398510886767 -22.94436624274295
VL30-3 0.0009003059035035132 4.741575958438955e-17
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.13588127171639178 0.03947926315947721
RT-VL30-4 -19.371894793640834 3.0858605446318728 -8.575292844546164 -25.47683277150537
VL30-5 0.24345611433658312 0.1148130957933935
RT-VL30-5 -17.884058542789816 4.491522085394682 -5.982822397202772 -25.47683277150537
VL30-all 0.1700869796709425 0.10623578808130991
RT-VL30-all -19.425733513734425 0.803855499578501 -17.884058542789816 -20.000024138165216
*** budget = 40
VL40-1 0.3136928145054432 0.013402566716908426
RT-VL40-1 -19.913971312317802 1.4589144658175337 -13.982398510886767 -22.94436624274295
VL40-2 0.15369281450544303 0.013402566716908424
RT-VL40-2 -19.913971312317802 1.4589144658175337 -13.982398510886767 -22.94436624274295
VL40-3 0.0009003059035035039 5.1971965818568465e-17
RT-VL40-3 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-4 0.13111629760072965 0.04195127380486602
RT-VL40-4 -19.210058616318186 3.4855279657223095 -8.575292844546164 -25.47683277150537
VL40-5 0.2317450831786567 0.12016433677018423
RT-VL40-5 -17.714041228078408 4.574636798403141 -5.982822397202772 -25.47683277150537
VL40-all 0.16622946313875522 0.10465330252857051
RT-VL40-all -19.350413321439483 0.8665566920350091 -17.714041228078408 -20.00002413816522
*** budget = 50
VL50-1 0.31254321263581175 0.014639196560207943
RT-VL50-1 -19.8030787063444 1.5351109732248942 -13.982398510886767 -22.94436624274295
VL50-2 0.15254321263581164 0.014639196560207946
RT-VL50-2 -19.8030787063444 1.5351109732248942 -13.982398510886767 -22.94436624274295
VL50-3 0.0009003059035034918 5.501551362679128e-17
RT-VL50-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-4 0.12668298719756352 0.04489775299529309
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VL50-5 0.2299983935080275 0.1212434428643659
RT-VL50-5 -17.441638464257423 4.176167100056399 -7.624671631899564 -23.151241280254865
VL50-all 0.16453362237614358 0.10444594567256596
RT-VL50-all -19.269294642394506 0.9428685465160097 -17.441638464257423 -20.000024138165216
logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00006_6_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.00/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00006_6_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.00/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00006_6_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.00/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.853056289631792 3.3399980285167636 -4.374563616678377 -38.725288137349295
LR -0.032337590639579056 0.0416352540202699 -3.1157654099001064e-06 -0.4917489599900311
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.07443101420594239 0.044971865344668446
RT-VL10-1 -10.227652054375197 3.3391747294403595 -6.368636980752588 -24.402634937426495
VL10-2 0.015895507971101244 0.014430181299784071
RT-VL10-2 -8.109158958037364 2.413830418185616 -6.173364844285768 -25.18063883520628
VL10-3 0.019759008748039052 0.016348917804918477
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VL10-4 0.1046521663171703 0.05541929788495704
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VL10-5 0.25939659048458064 0.06475238182281824
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VL10-all 0.09482685754536672 0.08883812693471778
RT-VL10-all -7.778980816689334 1.349910065101877 -6.486198304775274 -10.227652054375197
*** budget = 20
VL20-1 0.0474178114767869 0.03495726337581555
RT-VL20-1 -10.51585776611727 2.658640192890756 -7.41680489206008 -16.684221843834482
VL20-2 0.007502256477505482 0.006419086507769475
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VL20-3 0.010750237698935242 0.010720481173837487
RT-VL20-3 -8.30301150986369 4.909862786058228 -5.354012514882474 -21.276236018776252
VL20-4 0.07057116508790676 0.04874997148997731
RT-VL20-4 -6.95466193671011 3.921772089385125 -4.968012221835525 -22.480102359854264
VL20-5 0.22006001342272735 0.06203225908588504
RT-VL20-5 -6.534565714259021 3.0374940055947253 -4.968012221835525 -22.480102359854264
VL20-all 0.07126029683277235 0.0780215991044626
RT-VL20-all -8.079026662344392 1.3883756589841212 -6.534565714259021 -10.51585776611727
*** budget = 30
VL30-1 0.03424651749952013 0.029304743481071642
RT-VL30-1 -10.89322177609892 2.6888280695095554 -7.4205692515646255 -16.684221843834482
VL30-2 0.005332388677851874 0.004040365694239227
RT-VL30-2 -8.03276705943723 1.6760762625914314 -6.449960796620877 -16.0150407855712
VL30-3 0.006297916532518546 0.006647554109132885
RT-VL30-3 -9.028073050484737 5.56166856307747 -5.376078939388993 -21.276236018776252
VL30-4 0.05150395553361456 0.03694552175580519
RT-VL30-4 -7.504329328853005 4.697526158026058 -5.193976471126195 -22.480102359854264
VL30-5 0.2006786396543583 0.05175661305686185
RT-VL30-5 -6.856575739473187 3.667530464718417 -4.968012221835525 -22.480102359854264
VL30-all 0.05961188357957268 0.07266032288961685
RT-VL30-all -8.462993390869416 1.40783764524628 -6.856575739473187 -10.89322177609892
*** budget = 40
VL40-1 0.028778040913695523 0.028627136307599048
RT-VL40-1 -11.284603507019913 2.7159112036984556 -7.41680489206008 -16.684221843834482
VL40-2 0.00409759788452761 0.0037338934807409955
RT-VL40-2 -8.08136095692233 1.3008239229037293 -6.449960796620877 -13.48288812046589
VL40-3 0.004788498469631103 0.004876833582253463
RT-VL40-3 -9.631675815931752 5.971912886315703 -5.354012514882474 -21.276236018776252
VL40-4 0.03680562914609436 0.03336240999544988
RT-VL40-4 -7.083033119402968 4.15035348179304 -5.193976471126195 -21.647148453030304
VL40-5 0.18194804625834307 0.05258599400232971
RT-VL40-5 -6.247009985539414 2.3778986187869915 -4.968012221835525 -20.35920078867748
VL40-all 0.051283562534458337 0.06659973364479285
RT-VL40-all -8.465536676963275 1.804870412791772 -6.247009985539414 -11.284603507019913
*** budget = 50
VL50-1 0.022640003152381308 0.02411210783186333
RT-VL50-1 -11.804189386344927 2.684722317959785 -7.788028763502941 -16.684221843834482
VL50-2 0.0037386811488618775 0.003349692895215541
RT-VL50-2 -7.894450796417518 1.0118271449259235 -6.449960796620877 -9.687635960623401
VL50-3 0.003327310697158037 0.0029551492903951253
RT-VL50-3 -10.450987032219452 6.356211046191472 -5.354012514882474 -21.276236018776252
VL50-4 0.03530749995084913 0.03212014753037755
RT-VL50-4 -8.38174518584603 5.9058583508256985 -4.968012221835525 -22.480102359854264
VL50-5 0.17884320562404088 0.04935313433906037
RT-VL50-5 -6.3682013121609815 3.0139575689761 -4.968012221835525 -22.480102359854264
VL50-all 0.048771340114658245 0.0661451617939089
RT-VL50-all -8.979914742597781 1.9234171042398258 -6.3682013121609815 -11.804189386344927
logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00008_8_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s5/08.10_20.42.58/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00008_8_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s5/08.10_20.42.58/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/ablo/run_2024-08-10_20-42-24/run_93976_00008_8_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-08-10_20-42-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g0_s5/08.10_20.42.58/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.6342029468236845 1.9779318201441105 -4.347746806141463 -27.2754662044765
LR -0.046015321183098576 0.025994017285520615 -4.263551425731649e-05 -0.2875194295925234
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.09927085157903756 0.05480181417519518
RT-VL10-1 -6.127459939007751 1.8434066746065236 -4.347746806141463 -19.708422251875586
VL10-2 0.024246318636906496 0.019732074395356656
RT-VL10-2 -7.030643086206973 1.9430864567764938 -4.6003716384259175 -20.53433481403609
VL10-3 0.015328297886400803 0.013639755195465687
RT-VL10-3 -8.848638657015183 3.0286437228679937 -4.55073834521211 -23.452129229958402
VL10-4 0.04174686457590275 0.03393858683954163
RT-VL10-4 -7.529214714184979 0.5597022042730067 -5.624594834840639 -9.808696896296262
VL10-5 0.15827930794673023 0.06787538132165331
RT-VL10-5 -7.192920215717857 1.0306415263676618 -4.628769141141267 -11.169308503880258
VL10-all 0.06777432812499556 0.053862574982645864
RT-VL10-all -7.345775322426549 0.883087039461699 -6.127459939007751 -8.848638657015183
*** budget = 20
VL20-1 0.06592344616284397 0.043080736963504694
RT-VL20-1 -5.811521736180496 1.899073227619863 -4.347746806141463 -16.230672001085097
VL20-2 0.014106737296715025 0.013244017669364117
RT-VL20-2 -6.995337633859519 1.900897387438341 -4.924202029924122 -20.53433481403609
VL20-3 0.0076923725262972534 0.00753405294852983
RT-VL20-3 -8.895344609908872 2.9485601898823153 -7.771610739301692 -21.571164094990024
VL20-4 0.023679420909362023 0.020123418711474095
RT-VL20-4 -7.377593614628965 0.4176800120482282 -6.173440935749767 -8.420839478582595
VL20-5 0.11708328828569277 0.053082147312485235
RT-VL20-5 -6.773172409708488 1.192261539436846 -4.628769141141267 -11.169308503880258
VL20-all 0.04569705303618221 0.04106624276863511
RT-VL20-all -7.170594000857268 1.00532080844113 -5.811521736180496 -8.895344609908872
*** budget = 30
VL30-1 0.04770432245081056 0.031053621732369308
RT-VL30-1 -5.634950103414486 1.690112947817029 -4.407015772132561 -14.819188899592138
VL30-2 0.010207621538957401 0.009654024560585606
RT-VL30-2 -7.129979829933381 2.2940669494877675 -4.924202029924122 -20.53433481403609
VL30-3 0.005392660725817034 0.004432566382340304
RT-VL30-3 -8.995345683946772 3.1801492711647104 -7.7887674006994985 -21.571164094990024
VL30-4 0.015086068029388913 0.012075678895594313
RT-VL30-4 -7.422035916070323 0.32766692748307813 -6.845062197675135 -8.420839478582595
VL30-5 0.09698216120604569 0.04244538748607052
RT-VL30-5 -6.459520355417425 1.1490840716092718 -4.628769141141267 -11.09620345028805
VL30-all 0.03507456679020392 0.03432486437222344
RT-VL30-all -7.1283663777564765 1.117989561737718 -5.634950103414486 -8.995345683946772
*** budget = 40
VL40-1 0.04768273597929125 0.03416867553030484
RT-VL40-1 -5.820424854356761 1.9358042104688507 -4.347746806141463 -14.819188899592138
VL40-2 0.007353096312876761 0.005886249161339303
RT-VL40-2 -7.483762274888927 2.5683824838671514 -6.297619760346702 -20.53433481403609
VL40-3 0.00453935153352656 0.00417135479434921
RT-VL40-3 -8.726411792071817 2.7672540288977374 -7.7887674006994985 -21.571164094990024
VL40-4 0.012046549098096854 0.011839931719751874
RT-VL40-4 -7.461795478499513 0.46752558199065486 -6.876213108524244 -9.808696896296262
VL40-5 0.08334334221615293 0.03775605753760607
RT-VL40-5 -6.270733324026903 1.260930164665525 -4.628769141141267 -11.09620345028805
VL40-all 0.030993015027988867 0.03045230428886486
RT-VL40-all -7.152625544768784 1.0231845681691258 -5.820424854356761 -8.726411792071817
*** budget = 50
VL50-1 0.03594977446401717 0.024992538870653617
RT-VL50-1 -5.74957520783077 2.0559865455949318 -4.347746806141463 -14.819188899592138
VL50-2 0.0057025142177930714 0.005053707109832945
RT-VL50-2 -7.51280352053245 2.7789582918115885 -6.297619760346702 -20.53433481403609
VL50-3 0.0038926882420029546 0.0031702718997345114
RT-VL50-3 -8.462459990305875 1.9307686471607075 -7.818289776318878 -18.789898328891606
VL50-4 0.010703863522399404 0.01072005264581452
RT-VL50-4 -7.362780062356507 0.2562775810805993 -6.876213108524244 -7.897449307442641
VL50-5 0.07480296653807349 0.03617320976916685
RT-VL50-5 -6.179444417529796 1.3697333882208318 -4.628769141141267 -11.09620345028805
VL50-all 0.02621036139685722 0.026889400322804676
RT-VL50-all -7.0534126397110795 0.9752922763919828 -5.74957520783077 -8.462459990305875
logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00009_9_dr_cc=0.7000,seed=5_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s5/05.30_19.40.54/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00009_9_dr_cc=0.7000,seed=5_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s5/05.30_19.40.54/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -6.913672864025125 4.162086032996592 -3.6494306875445526 -35.58667902032048
LR -0.028498540408053188 0.049629460241707155 -0.00011173433462197124 -0.4739910478173399
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.1340619679325045 0.04243478024776506
RT-VL10-1 -8.61835552258116 3.0289536662119807 -4.612261506856492 -24.189119734656753
VL10-2 0.018719575586869285 0.019210672231449934
RT-VL10-2 -7.850790128471154 3.3482903673105757 -4.612261506856492 -24.189119734656753
VL10-3 0.00827806053497189 0.009826941440746086
RT-VL10-3 -10.687980801947804 7.258068899876575 -4.460300363590081 -27.18291099139566
VL10-4 0.05995357627288056 0.04230344840094223
RT-VL10-4 -5.2085277190950805 2.7920457356997397 -3.6494306875445526 -20.000024138165212
VL10-5 0.18005067088469648 0.07851880253233179
RT-VL10-5 -5.257592677542745 2.781815930559909 -3.6494306875445526 -20.000024138165212
VL10-all 0.08021277024238453 0.0666840204043796
RT-VL10-all -7.524649369927589 2.0886929999157235 -5.2085277190950805 -10.687980801947804
*** budget = 20
VL20-1 0.10862525854675063 0.029709353179927355
RT-VL20-1 -9.500756879779232 2.9882670351202063 -5.83135028462317 -24.189119734656753
VL20-2 0.008714432742232032 0.007334537648215124
RT-VL20-2 -8.090370751196595 3.535895060506388 -5.1052806866859 -24.189119734656753
VL20-3 0.0033112415571904 0.005154417542162535
RT-VL20-3 -12.271748589925215 7.443701752685664 -4.467701593932773 -20.71683497594264
VL20-4 0.03683974997366671 0.02625601608431374
RT-VL20-4 -4.58052413424749 0.5503079291778845 -3.758453927274629 -6.61757973710269
VL20-5 0.13627899139560196 0.06785554902696528
RT-VL20-5 -4.749854474268959 0.5764506115388285 -3.774634820578871 -6.3257928730656765
VL20-all 0.05875393484308835 0.053954348905742244
RT-VL20-all -7.838650965883498 2.9200810838484035 -4.58052413424749 -12.271748589925215
*** budget = 30
VL30-1 0.10112195171294733 0.028572441470854935
RT-VL30-1 -9.512272841568665 2.5956493988958673 -5.83135028462317 -17.387240732469227
VL30-2 0.005684437993235012 0.005210383984099605
RT-VL30-2 -8.081740374023362 3.538838827468683 -5.325982891889392 -24.189119734656753
VL30-3 0.001937418801615136 0.003399479899028786
RT-VL30-3 -12.656616692958421 7.3984211307135 -4.467701593932773 -20.71683497594264
VL30-4 0.027418425316811074 0.022721406117423727
RT-VL30-4 -4.5573976503927955 0.5461801876101497 -3.7450228542365167 -6.61757973710269
VL30-5 0.11129480575897903 0.06063190069636676
RT-VL30-5 -4.688591573099332 0.5529158536334413 -3.758453927274629 -6.264420917049495
VL30-all 0.04949140791671751 0.04722927501674191
RT-VL30-all -7.899323826408515 3.0575803475577006 -4.5573976503927955 -12.656616692958421
*** budget = 40
VL40-1 0.09043440304077485 0.026100012424203633
RT-VL40-1 -9.452623995369414 1.9868769332702116 -6.496040308439997 -14.096314316687568
VL40-2 0.00510223243106562 0.005165334618327393
RT-VL40-2 -8.30773932935163 3.866634457592247 -5.591618041083613 -24.189119734656753
VL40-3 0.0011373344216348415 0.001462668666380431
RT-VL40-3 -12.378950005777376 7.487263553406503 -4.467701593932773 -20.71683497594264
VL40-4 0.022326429947550925 0.019210816009993185
RT-VL40-4 -4.480604668738878 0.4609696563619304 -3.7450228542365167 -5.947385044542316
VL40-5 0.09357025506716704 0.05185389558962467
RT-VL40-5 -4.7405911618448595 0.6067643017938816 -3.774634820578871 -6.264420917049495
VL40-all 0.042514130981638656 0.04104220510797762
RT-VL40-all -7.8721018322164324 2.9768480943838886 -4.480604668738878 -12.378950005777376
*** budget = 50
VL50-1 0.08770749127533133 0.026849406700136264
RT-VL50-1 -9.32021192505498 1.8233080131530028 -6.496040308439997 -13.623071468923737
VL50-2 0.0036225830340909536 0.004526013187872506
RT-VL50-2 -8.20087180917088 3.762381026843614 -5.591618041083613 -24.189119734656753
VL50-3 0.0011105902328255738 0.0015463491694528014
RT-VL50-3 -12.584805784793263 7.449783202208587 -4.467701593932773 -20.000024138165212
VL50-4 0.017385604232924734 0.015025052233787256
RT-VL50-4 -4.477323217443415 0.46484251017996875 -3.774634820578871 -5.947385044542316
VL50-5 0.08213312993243575 0.0486965895671633
RT-VL50-5 -4.797174358418858 0.5463909705058057 -3.9042190213332413 -6.264420917049495
VL50-all 0.03839187974152167 0.03843276898929195
RT-VL50-all -7.876077418976278 3.0131150946933865 -4.477323217443415 -12.584805784793263
logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00007_7_dr_cc=0.7000,seed=4_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s4/05.30_19.40.54/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00007_7_dr_cc=0.7000,seed=4_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s4/05.30_19.40.54/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.766492747512235 1.762577218092476 -4.260418532487382 -20.000024138165212
LR -0.027469140578151974 0.025920772441159385 -1.4918871330982242e-05 -0.2000004781111453
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.06702887331719627 0.04555828451085621
RT-VL10-1 -8.700650980110703 1.071167319414357 -5.607261847643933 -12.997449155709518
VL10-2 0.01633763190241132 0.01548955193109197
RT-VL10-2 -7.700344283889819 0.9994309389227486 -5.387269710724919 -9.255112050116752
VL10-3 0.01823900700492135 0.018269929722279393
RT-VL10-3 -8.774711781903726 4.065194465280514 -5.324719020752292 -20.000024138165212
VL10-4 0.07446296846871088 0.05362768529077217
RT-VL10-4 -7.294697915667506 2.3447806911471085 -4.60557793709098 -20.000024138165212
VL10-5 0.21562356190473206 0.0745133430449997
RT-VL10-5 -7.278498605988212 2.3525389404238184 -4.60557793709098 -20.000024138165212
VL10-all 0.07833840851959437 0.07272712331120869
RT-VL10-all -7.949780713511993 0.6612547311280172 -7.278498605988212 -8.774711781903726
*** budget = 20
VL20-1 0.0428468554892907 0.030156620556522386
RT-VL20-1 -8.97531114612965 1.0691629056967624 -5.607261847643933 -12.997449155709518
VL20-2 0.0076559314097000425 0.006603230090078259
RT-VL20-2 -7.7036518041974995 1.009499290186598 -5.793152048595687 -9.195609552844507
VL20-3 0.008029091923042431 0.008109935286651168
RT-VL20-3 -9.871807877762015 5.072897632763505 -5.324719020752292 -20.000024138165212
VL20-4 0.04395394827323055 0.03347167394923322
RT-VL20-4 -6.783668352344567 0.6125955825055617 -4.60557793709098 -8.199763772914904
VL20-5 0.17508363992471335 0.06280846811927979
RT-VL20-5 -6.78666011462384 0.6019843543076149 -5.432230607679755 -8.199763772914904
VL20-all 0.055513893403995415 0.06186469326186065
RT-VL20-all -8.024219859011515 1.2240459274759417 -6.783668352344567 -9.871807877762015
*** budget = 30
VL30-1 0.03454058424883677 0.028380023927971745
RT-VL30-1 -9.000390973786336 0.9588357967421189 -5.607261847643933 -10.646823823659597
VL30-2 0.005871932112434848 0.005420692883832742
RT-VL30-2 -7.572839338932881 1.034033640582295 -5.793152048595687 -8.998279873074763
VL30-3 0.005631495800975119 0.007167411624283474
RT-VL30-3 -11.084100154181776 5.882224026827701 -5.324719020752292 -20.000024138165212
VL30-4 0.029903488472027114 0.025540104168319862
RT-VL30-4 -6.587149267609132 0.5664399508611941 -4.60557793709098 -7.797229756582811
VL30-5 0.14986546941891138 0.057773694543808124
RT-VL30-5 -6.602961084674619 0.6278962669583686 -4.60557793709098 -7.751081590808975
VL30-all 0.04516259401063705 0.0536932460371686
RT-VL30-all -8.169488163836949 1.7028890642885317 -6.587149267609132 -11.084100154181776
*** budget = 40
VL40-1 0.026078891165746625 0.024391023881792494
RT-VL40-1 -9.05025337604879 1.3165381889849226 -5.607261847643933 -12.997449155709518
VL40-2 0.003688049803583405 0.0028412948245769036
RT-VL40-2 -7.6015678262025315 0.9845411081999992 -5.793152048595687 -8.998279873074763
VL40-3 0.003980265934034948 0.005739279282296423
RT-VL40-3 -11.992459165115775 6.101257785315389 -5.38577375074818 -20.000024138165212
VL40-4 0.02233300914185217 0.01804514985264858
RT-VL40-4 -6.600805429332899 0.5728807980765679 -4.60557793709098 -7.797229756582811
VL40-5 0.13357644535045984 0.051372568390208304
RT-VL40-5 -6.674892655431074 0.5478607238484342 -5.502652958204479 -7.719758914704076
VL40-all 0.0379313322791354 0.04869713133263139
RT-VL40-all -8.383995690426215 2.008875407088309 -6.600805429332899 -11.992459165115775
*** budget = 50
VL50-1 0.022951151001508338 0.021783176743366946
RT-VL50-1 -9.08621776117241 1.2598835235397619 -6.372640992254461 -12.997449155709518
VL50-2 0.0035989583911486506 0.004140754637959294
RT-VL50-2 -7.78622514185185 0.982204104544821 -5.793152048595687 -8.998279873074763
VL50-3 0.002319151964187812 0.0030471777940402124
RT-VL50-3 -13.235861714030072 6.3589090876599625 -5.38577375074818 -20.000024138165212
VL50-4 0.020005672058401584 0.020468874908642767
RT-VL50-4 -6.612798568135066 0.5808166705318218 -4.60557793709098 -7.797229756582811
VL50-5 0.12690515215032544 0.05474397389347829
RT-VL50-5 -6.570198469941904 0.5949466057275482 -5.502652958204479 -7.719758914704076
VL50-all 0.03515601711311437 0.046627252031716096
RT-VL50-all -8.658260331026261 2.467466048966801 -6.570198469941904 -13.235861714030072
logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00005_5_dr_cc=0.7000,seed=3_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s3/05.30_19.40.54/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00005_5_dr_cc=0.7000,seed=3_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s3/05.30_19.40.54/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -5.855622143118182 2.9839446515873314 -4.504105860755017 -61.20465360601212
LR -0.013325370614859423 0.044512054196238056 -5.424463503489463e-06 -0.9073699497679178
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.15395968856673936 0.06476064267636067
RT-VL10-1 -6.505472761366334 4.445302151432614 -4.572851681985835 -49.089756743126344
VL10-2 0.037704892159211086 0.03311245328821899
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VL10-3 0.014800125568325363 0.012397862208833673
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VL10-4 0.02689534926345143 0.025804559979166072
RT-VL10-4 -5.736974783572854 0.7593233473272202 -4.637210447515905 -8.478202374647672
VL10-5 0.12760957351761743 0.057647451216882084
RT-VL10-5 -6.045741619309115 1.3113884467219818 -4.637210447515905 -17.09860378275986
VL10-all 0.07219392581506894 0.05708246097626866
RT-VL10-all -5.924070215301059 0.3682518943963746 -5.385819630543125 -6.505472761366334
*** budget = 20
VL20-1 0.11840248432522343 0.049117361370293246
RT-VL20-1 -7.053419734716851 5.569334743358752 -4.906629840606344 -49.089756743126344
VL20-2 0.019069538567258744 0.016081258212881526
RT-VL20-2 -5.998345202465637 2.5455410607221247 -4.906629840606344 -27.558552554623788
VL20-3 0.007852437191442378 0.0057722496840013045
RT-VL20-3 -5.466027141123269 0.5583781952747662 -4.592437634702186 -6.785667580968682
VL20-4 0.01245547506290342 0.012246979984789136
RT-VL20-4 -5.838764605361739 0.7946502072555091 -4.645569843001535 -8.478202374647672
VL20-5 0.09546169096325142 0.046600348935235104
RT-VL20-5 -6.357229146937526 1.6483302250312155 -4.645569843001535 -17.09860378275986
VL20-all 0.05064832522201588 0.04666104462403081
RT-VL20-all -6.1427571661210045 0.5378725431501701 -5.466027141123269 -7.053419734716851
*** budget = 30
VL30-1 0.1007135434321274 0.04587428279552922
RT-VL30-1 -7.806978072444244 6.798042212405201 -5.169297841225459 -49.089756743126344
VL30-2 0.01492368499062412 0.014193641983997194
RT-VL30-2 -6.179720727098697 3.0850690480088363 -4.960451412455809 -27.558552554623788
VL30-3 0.006353829837299168 0.0051025890128728124
RT-VL30-3 -5.535662542444888 0.67057881396088 -4.592437634702186 -7.726368341416492
VL30-4 0.009516850191244187 0.010590609293911398
RT-VL30-4 -5.845257977762791 0.7978370518226693 -4.645569843001535 -8.478202374647672
VL30-5 0.07931473846937077 0.04296466411753146
RT-VL30-5 -6.211032458123835 1.2076347416697537 -4.8296757034578235 -11.14382037763997
VL30-all 0.042164529384133134 0.03974535405064567
RT-VL30-all -6.315730355574892 0.7852553817965923 -5.535662542444888 -7.806978072444244
*** budget = 40
VL40-1 0.08992192315794796 0.04017015410609063
RT-VL40-1 -7.844622454003711 6.984708459619323 -5.257905488148211 -49.089756743126344
VL40-2 0.009267762927125512 0.00868978315783272
RT-VL40-2 -5.6349845064487685 0.3276248812221012 -4.960451412455809 -6.461867131265564
VL40-3 0.004631582659412268 0.0033449583417107017
RT-VL40-3 -5.66370675355164 0.6750833929810242 -4.634684531112255 -7.726368341416492
VL40-4 0.007247854571071736 0.007699915359721891
RT-VL40-4 -5.926060323748237 0.8572210419824725 -4.645569843001535 -8.478202374647672
VL40-5 0.0659655682055466 0.03860425697959273
RT-VL40-5 -6.3616972907396345 1.1734272593553037 -5.10203979298873 -11.14382037763997
VL40-all 0.035406938304220814 0.03557815744582003
RT-VL40-all -6.286214265698398 0.8216642175925809 -5.6349845064487685 -7.844622454003711
*** budget = 50
VL50-1 0.08260793570197023 0.039534247654546904
RT-VL50-1 -8.467264135525529 7.6883940791125225 -5.37640195085382 -49.089756743126344
VL50-2 0.007621906752702327 0.006092715990194937
RT-VL50-2 -5.6323667174208945 0.29388072118812947 -4.960451412455809 -6.132788688707919
VL50-3 0.004205647757772665 0.003028735192919862
RT-VL50-3 -5.664090416203682 0.6702101691153051 -4.592437634702186 -7.726368341416492
VL50-4 0.004052197658847182 0.003962052823152059
RT-VL50-4 -5.972016716829018 0.8790239289563785 -4.652263061940726 -8.478202374647672
VL50-5 0.05572480733778399 0.03477912017903522
RT-VL50-5 -6.919115041402683 2.3182634255823737 -4.883902871132553 -17.09860378275986
VL50-all 0.03084249904181528 0.03245066316588252
RT-VL50-all -6.5309706054763605 1.074363848746471 -5.6323667174208945 -8.467264135525529
logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00003_3_dr_cc=0.7000,seed=2_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s2/05.30_19.40.54/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00003_3_dr_cc=0.7000,seed=2_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s2/05.30_19.40.54/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -8.189932474876844 3.4064956444533188 -5.508117149197284 -32.8724904991699
LR -0.047724038889924957 0.047043729636058525 -0.0001469342676299057 -0.4128005727817543
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.044762280771790695 0.04096174035346412
RT-VL10-1 -6.615389939030943 0.5597135337541318 -6.033247591846271 -11.15649410304566
VL10-2 0.02032971097806103 0.019304426713486967
RT-VL10-2 -6.81005129752618 0.5330226625674654 -5.793781229484763 -8.567462651279921
VL10-3 0.013649803712490855 0.020776493842172474
RT-VL10-3 -13.684095730039992 5.948169975550092 -6.1596239658819645 -20.000024138165212
VL10-4 0.05690758143175806 0.046945065886747475
RT-VL10-4 -8.69102747021417 2.7933420720646 -6.527675512698679 -20.000024138165212
VL10-5 0.17879575923547417 0.07868158506044844
RT-VL10-5 -8.723856360215754 3.473754373063845 -6.069268828576396 -32.8724904991699
VL10-all 0.06288902722591497 0.06005751191587294
RT-VL10-all -8.904884159405409 2.551449685524524 -6.615389939030943 -13.684095730039992
*** budget = 20
VL20-1 0.018853287082215613 0.018585689191880445
RT-VL20-1 -6.697801260942175 0.6636923891535611 -6.057935984577092 -11.15649410304566
VL20-2 0.010928176854949852 0.01237115790770996
RT-VL20-2 -6.8223892538207656 0.4984123427267538 -5.9403123641611035 -8.567462651279921
VL20-3 0.004012938014386461 0.009334609530191483
RT-VL20-3 -16.93585491130027 5.140803135943424 -7.688954229210025 -20.000024138165212
VL20-4 0.03444052006410624 0.028924831794490045
RT-VL20-4 -7.998630983254558 0.47231880043996166 -6.527675512698679 -9.052363445012821
VL20-5 0.13708064442318105 0.06235423255140064
RT-VL20-5 -8.041570310392418 2.9691939818488526 -6.069268828576396 -32.8724904991699
VL20-all 0.04106311328776784 0.049065937855145965
RT-VL20-all -9.299249343942037 3.8598828297650094 -6.697801260942175 -16.93585491130027
*** budget = 30
VL30-1 0.011942861218555123 0.010550115197868993
RT-VL30-1 -6.72834572635369 0.7344954150553712 -6.057935984577092 -11.15649410304566
VL30-2 0.006542394604859503 0.0075902257407215274
RT-VL30-2 -6.888862299568366 0.5243879332387416 -5.9978839198298255 -8.567462651279921
VL30-3 0.001061864865393325 0.0011376801441326141
RT-VL30-3 -18.35890817001328 4.07150868514208 -7.688954229210025 -20.000024138165212
VL30-4 0.02178233872489284 0.020727475209471442
RT-VL30-4 -8.082907691665834 0.44884799867320413 -7.098465654484826 -9.052363445012821
VL30-5 0.10973475305618895 0.04881145727232571
RT-VL30-5 -8.072275280595104 3.633363404156769 -6.069268828576396 -32.8724904991699
VL30-all 0.03021284249397795 0.04034518427035092
RT-VL30-all -9.626259833639256 4.4033442342099915 -6.72834572635369 -18.35890817001328
*** budget = 40
VL40-1 0.008428310411468345 0.0073340952152798075
RT-VL40-1 -6.811583726597351 0.8446988155372162 -6.057935984577092 -11.15649410304566
VL40-2 0.004750864432252243 0.006648103488694433
RT-VL40-2 -6.860060792866537 0.48543313265143073 -5.9978839198298255 -8.567462651279921
VL40-3 0.0009413476394259124 0.0005712925840199839
RT-VL40-3 -18.114494528571633 4.291680905938221 -7.688954229210025 -20.000024138165212
VL40-4 0.019126010346791227 0.019577075743520436
RT-VL40-4 -8.126069977482269 0.4565098181818999 -7.098465654484826 -8.973189261361632
VL40-5 0.09573661310451079 0.044737258175281826
RT-VL40-5 -8.090735560582122 4.20322420347258 -6.069268828576396 -32.8724904991699
VL40-all 0.025796629186889703 0.035492157550937535
RT-VL40-all -9.600588917219984 4.294868947357839 -6.811583726597351 -18.114494528571633
*** budget = 50
VL50-1 0.008373203147910077 0.009337769781642606
RT-VL50-1 -6.798122304018029 0.9192357070228864 -6.057935984577092 -11.15649410304566
VL50-2 0.0030797038114827664 0.002484131394916913
RT-VL50-2 -6.837472431876047 0.5384067104404726 -5.9978839198298255 -8.567462651279921
VL50-3 0.0009557694014803293 0.0006331084946389884
RT-VL50-3 -18.083236481140435 4.29136770918869 -7.688954229210025 -20.000024138165212
VL50-4 0.012841222528865597 0.012650862027730306
RT-VL50-4 -8.0518059122361 0.44284549267497003 -7.098465654484826 -8.973189261361632
VL50-5 0.08521907671598727 0.038122629612646015
RT-VL50-5 -8.177353591155638 4.659010371447075 -6.069268828576396 -32.8724904991699
VL50-all 0.02209379512114521 0.031833999810758656
RT-VL50-all -9.58959814408525 4.286435935081512 -6.798122304018029 -18.083236481140435
logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00001_1_dr_cc=0.7000,seed=1_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s1/05.30_19.40.53/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/ig/run_2024-05-30_19-40-22/run_fb142_00001_1_dr_cc=0.7000,seed=1_2024-05-30_19-40-22/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg00_ds0.001_s1/05.30_19.40.53/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -8.528741401213724 4.834251133054918 -4.310114888419717 -38.579291424260816
LR -0.04249867794585417 0.07831361408846327 -5.1428342737969146e-05 -0.8651421632669791
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.18012444285663098 0.06787527847679778
RT-VL10-1 -9.705436995490894 5.773121019201364 -4.310114888419717 -27.511323810294115
VL10-2 0.04859910024321007 0.042141153598062565
RT-VL10-2 -9.647189260117276 5.859387821034273 -4.342679436015779 -27.511323810294115
VL10-3 0.015024051141356983 0.015247886545771373
RT-VL10-3 -10.88671588201081 6.501854506609797 -5.283198092377768 -33.29415179442295
VL10-4 0.020873723297996194 0.017061448660311344
RT-VL10-4 -6.748813065540907 1.4563641346267568 -5.077238061030261 -21.333213773156082
VL10-5 0.08371077117713742 0.058160527800285716
RT-VL10-5 -8.00666080538876 3.8474441970051125 -5.077238061030261 -38.284947948987266
VL10-all 0.06966641774326635 0.060346609351404845
RT-VL10-all -8.998963201709731 1.4512582201060538 -6.748813065540907 -10.88671588201081
*** budget = 20
VL20-1 0.14613072724861242 0.057758350314849244
RT-VL20-1 -9.535829561189908 5.352376273456817 -4.310114888419717 -25.87262043481669
VL20-2 0.02845697735839652 0.025322950002157122
RT-VL20-2 -8.792790676845506 4.91600475252326 -4.342679436015779 -25.41964836017108
VL20-3 0.006902606341310345 0.007647621547240221
RT-VL20-3 -11.524811275967755 7.1842555526817495 -5.362395199459256 -33.29415179442295
VL20-4 0.011635659921848559 0.010318153856658439
RT-VL20-4 -6.892386558933605 1.898300796647549 -5.336838290251412 -21.333213773156082
VL20-5 0.05230076866135261 0.04002012029214638
RT-VL20-5 -8.49892689839713 3.895834670091505 -5.375189025292185 -35.10714183267867
VL20-all 0.049085347906304096 0.051059841499672576
RT-VL20-all -9.04894899426678 1.5090294663402726 -6.892386558933605 -11.524811275967755
*** budget = 30
VL30-1 0.127688971482622 0.054469065509618775
RT-VL30-1 -9.552346334432409 4.969768177445222 -4.310114888419717 -24.04375865619039
VL30-2 0.020302598762731994 0.020029986224596577
RT-VL30-2 -8.711204506800051 5.382360108534246 -4.342679436015779 -25.41964836017108
VL30-3 0.004806074850434352 0.0058327551828257045
RT-VL30-3 -12.793863714649127 7.556820856886786 -5.367740718268767 -33.29415179442295
VL30-4 0.008245990890699759 0.0064960651389355
RT-VL30-4 -6.615590903362239 0.9138613140256785 -5.130081395816176 -10.136248077414983
VL30-5 0.0419207882719283 0.03460872797591027
RT-VL30-5 -8.422578443082651 2.6695229876438917 -5.57124191831845 -22.622995200873774
VL30-all 0.04059288485168329 0.045444921965996546
RT-VL30-all -9.219116780465296 2.027838887707757 -6.615590903362239 -12.793863714649127
*** budget = 40
VL40-1 0.11449408353031551 0.05258869167675601
RT-VL40-1 -9.34741463994479 5.020885545180404 -4.342679436015779 -23.73925999378669
VL40-2 0.015763734725437593 0.01755349547757008
RT-VL40-2 -8.178345860822962 4.841021961405505 -4.342679436015779 -24.66247921711314
VL40-3 0.003085523724357562 0.0033345419907014934
RT-VL40-3 -12.557078824288208 6.772952615781865 -5.362395199459256 -23.970771212920315
VL40-4 0.006413902269351015 0.005257265191646447
RT-VL40-4 -6.6852132497035415 0.9882983116912138 -5.48914236970864 -10.136248077414983
VL40-5 0.030833540177714124 0.02462009447398086
RT-VL40-5 -8.793038137204524 3.0173679075901925 -5.375189025292185 -22.622995200873774
VL40-all 0.03411815688543516 0.04132435879359793
RT-VL40-all -9.112218142392805 1.9383874005607682 -6.6852132497035415 -12.557078824288208
*** budget = 50
VL50-1 0.10180467294756339 0.04867981526553207
RT-VL50-1 -9.215067405999424 4.813820191127357 -4.310114888419717 -19.124241616741045
VL50-2 0.011644483602530061 0.01132054463501748
RT-VL50-2 -7.868246822387367 4.860942108222082 -4.342679436015779 -25.41964836017108
VL50-3 0.0020700437145618716 0.0024641543863414307
RT-VL50-3 -13.062824154762298 7.082234235887354 -5.367740718268767 -23.970771212920315
VL50-4 0.005318067851295438 0.0046091974823854725
RT-VL50-4 -6.613917183850406 1.0314055263022086 -5.4917350903607005 -10.136248077414983
VL50-5 0.028251971696244896 0.026243469981852705
RT-VL50-5 -8.711358591791827 1.8992083048534574 -5.984961032880961 -14.264567593761209
VL50-all 0.02981784796243913 0.03710796674071718
RT-VL50-all -9.094282831758264 2.1709867877495057 -6.613917183850406 -13.062824154762298
logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00037_37_dl_scale=0.5000,dr_cc=0.7000,seed=5_2024-05-30_19-40-14/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s5/05.31_08.34.13/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00037_37_dl_scale=0.5000,dr_cc=0.7000,seed=5_2024-05-30_19-40-14/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s5/05.31_08.34.13/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -8.14144446864733 3.7350710148226023 -5.469059031837276 -44.583066931552736
LR -0.034366271844236886 0.05210521407756716 -1.1578759480102008e-05 -0.6020735402268929
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.11267625155884366 0.0420690426724283
RT-VL10-1 -7.351340629603856 1.610735566713361 -5.887129896996914 -18.826135821978244
VL10-2 0.012535919495982473 0.023216886919403757
RT-VL10-2 -6.833392612218106 0.5133869576403489 -5.887129896996914 -11.276097993351508
VL10-3 0.030084034859834505 0.03903972462420731
RT-VL10-3 -13.401131306283911 6.2842125956558545 -6.514214453611547 -29.24814040136885
VL10-4 0.012618059384790306 0.012796089443933676
RT-VL10-4 -7.279081286494517 0.7418171974921458 -5.670303634653149 -11.242927215717188
VL10-5 0.09607773393370413 0.04648902696582113
RT-VL10-5 -6.70117840577682 1.4829369756292603 -5.6556826735160834 -20.6157554819564
VL10-all 0.05279839984663102 0.042918358181690534
RT-VL10-all -8.313224848075443 2.5561714911794784 -6.70117840577682 -13.401131306283911
*** budget = 20
VL20-1 0.09103561369328622 0.027505663031781834
RT-VL20-1 -7.601406471010568 1.617156157049929 -5.960478538630639 -13.758169304169671
VL20-2 0.005228512415535642 0.00629232396741213
RT-VL20-2 -6.7637155663861215 0.26337721197964503 -6.046483491756797 -7.52871583854249
VL20-3 0.010934045727687034 0.01996302552714993
RT-VL20-3 -15.594437573741189 6.016678100035232 -6.514214453611547 -22.66501921592259
VL20-4 0.005598249643514682 0.004254632878068425
RT-VL20-4 -7.342352287675533 0.7201034572262012 -6.587809349863319 -11.220533253307885
VL20-5 0.07236385607034361 0.04157484040178944
RT-VL20-5 -6.558964894710664 1.8874159084271063 -5.6556826735160834 -20.6157554819564
VL20-all 0.037032055510073436 0.03700101632794029
RT-VL20-all -8.772175358704814 3.431925606094849 -6.558964894710664 -15.594437573741189
*** budget = 30
VL30-1 0.0832502910009494 0.02735179554080795
RT-VL30-1 -8.224329747361956 2.3727696872679296 -6.213734961902845 -18.826135821978244
VL30-2 0.002880440539673892 0.003383723278839125
RT-VL30-2 -6.732688093621593 0.22814095380019472 -6.224887535824027 -7.247406417637632
VL30-3 0.004903344917311119 0.011464795900961043
RT-VL30-3 -17.46233411764304 4.901700834025685 -7.153065162333494 -20.043358041641024
VL30-4 0.0041791653464865195 0.00329839129743361
RT-VL30-4 -7.4066517937953185 0.7439610238263169 -6.796888305507794 -11.220533253307885
VL30-5 0.055848793367563994 0.03896430443662097
RT-VL30-5 -6.536431016367143 2.0648829355390133 -5.6556826735160834 -20.6157554819564
VL30-all 0.030212407034396982 0.033273280963467385
RT-VL30-all -9.272486953757811 4.137394138434256 -6.536431016367143 -17.46233411764304
*** budget = 40
VL40-1 0.07481653173870383 0.024757890092928673
RT-VL40-1 -8.282789129254011 1.9838969565478488 -6.247305705656351 -13.758169304169671
VL40-2 0.0023832587906394464 0.003034375621289443
RT-VL40-2 -6.792460407854841 0.4015951398403519 -6.406447420630532 -8.857228364944204
VL40-3 0.004932468626157369 0.012819827234092997
RT-VL40-3 -17.959324080281316 4.596306217934325 -7.4785323620480755 -22.66501921592259
VL40-4 0.002923086336598912 0.002465485891544446
RT-VL40-4 -7.306876974764291 0.5489640001733866 -6.796888305507794 -10.158013908405595
VL40-5 0.04819393128522742 0.03975692881728233
RT-VL40-5 -6.531849469565933 2.371703026815658 -5.6556826735160834 -20.6157554819564
VL40-all 0.02664985535546539 0.029690564443068303
RT-VL40-all -9.374660012344078 4.333905685515785 -6.531849469565933 -17.959324080281316
*** budget = 50
VL50-1 0.06906735707411236 0.02274215257783697
RT-VL50-1 -8.49001455807457 2.028023760592533 -6.280281189696415 -13.758169304169671
VL50-2 0.0014453486967966962 0.0016145264173186612
RT-VL50-2 -6.744050071180171 0.21273573261530537 -6.401872606475682 -7.164254276900512
VL50-3 0.0015044744050419297 0.0025225615993206053
RT-VL50-3 -18.63998704062571 3.6914218251614375 -7.684856873427425 -20.043358041641024
VL50-4 0.002573407686367923 0.001978045975607901
RT-VL50-4 -7.351182774643819 0.586701418579276 -6.8589294048708584 -10.158013908405595
VL50-5 0.04246949095101572 0.03388286462136762
RT-VL50-5 -6.866957998924005 2.885537747553752 -5.6556826735160834 -20.6157554819564
VL50-all 0.02341201576266693 0.027728396880077787
RT-VL50-all -9.618438488689655 4.552688897240212 -6.744050071180171 -18.63998704062571
logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00029_29_dl_scale=0.5000,dr_cc=0.7000,seed=4_2024-05-30_19-40-13/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s4/05.31_08.31.01/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00029_29_dl_scale=0.5000,dr_cc=0.7000,seed=4_2024-05-30_19-40-13/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s4/05.31_08.31.01/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.24758402086587 3.012670304377925 -4.400228391869956 -39.375316709659785
LR -0.021854742924440885 0.07574268354236206 -2.3112769997446492e-05 -1.0669874363865657
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.08697124068993038 0.04643201976280304
RT-VL10-1 -8.23355598290166 1.9034294889984982 -5.164511164745325 -14.516817169701048
VL10-2 0.021538186702468966 0.020194504059890435
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VL10-3 0.0398127290233631 0.0351222057863554
RT-VL10-3 -7.506455774817684 3.413087201393402 -4.776995608273122 -26.476124834579053
VL10-4 0.027365131031978875 0.022442162424571976
RT-VL10-4 -6.91302793538138 3.134901247017003 -4.7279891091074955 -29.01371053089753
VL10-5 0.05593586977678282 0.04356486000683917
RT-VL10-5 -6.762782218122159 1.0234790552707633 -5.593743188325231 -16.24543525755718
VL10-all 0.046324631444904826 0.023497906789393834
RT-VL10-all -7.207594368058038 0.594859277261183 -6.6221499290673105 -8.23355598290166
*** budget = 20
VL20-1 0.06108745083619966 0.03992334306666259
RT-VL20-1 -9.028744470512969 2.234982645567699 -5.164511164745325 -14.516817169701048
VL20-2 0.011137189785802276 0.012264413116256203
RT-VL20-2 -6.540418605191941 0.5455136658895786 -6.016705638491989 -9.261538220531017
VL20-3 0.020784469065316356 0.020091360985910314
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VL20-4 0.013784641049348867 0.010382450398857079
RT-VL20-4 -6.6261110025007435 2.615645359951906 -4.7279891091074955 -28.816451411278145
VL20-5 0.03197875598858632 0.028338894188092205
RT-VL20-5 -6.8145596634154 1.1307141389983726 -6.214501992265549 -16.24543525755718
VL20-all 0.027754501345050697 0.018159312978149898
RT-VL20-all -7.285405249983813 0.9239014115545715 -6.540418605191941 -9.028744470512969
*** budget = 30
VL30-1 0.04778590546550758 0.03247211243218947
RT-VL30-1 -9.566946312349593 2.3545303189479854 -5.164511164745325 -14.516817169701048
VL30-2 0.008161213811295271 0.009673320061325399
RT-VL30-2 -6.5479970455078345 0.5234630399578342 -6.016705638491989 -8.809692385972339
VL30-3 0.015649313810577024 0.01686255163675371
RT-VL30-3 -7.558924163004504 3.5938481735849375 -6.294817838626114 -26.476124834579053
VL30-4 0.010152297493531678 0.007953535277433983
RT-VL30-4 -6.35691458725362 0.40950334894408136 -4.7279891091074955 -7.123878974544381
VL30-5 0.02137231102289502 0.017301800464184634
RT-VL30-5 -6.73474523955603 0.25534445660236094 -6.214501992265549 -7.783632894554249
VL30-all 0.020624208320761315 0.01433913646869817
RT-VL30-all -7.353105469534316 1.1803743333616863 -6.35691458725362 -9.566946312349593
*** budget = 40
VL40-1 0.03768063607482232 0.029609912026052468
RT-VL40-1 -9.762651764082259 2.443821084368928 -6.535312634428026 -13.917296042158783
VL40-2 0.0054584959982648465 0.004963427933069067
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VL40-3 0.01061540263923666 0.009670004161305592
RT-VL40-3 -7.058060094554676 1.3800833799521135 -6.294817838626114 -15.200798782084451
VL40-4 0.008391530983519392 0.007499883749730485
RT-VL40-4 -6.8071474053697125 2.8133265293638194 -4.7279891091074955 -23.519263284966854
VL40-5 0.016088138264239896 0.011327084399183323
RT-VL40-5 -6.723222995076171 0.2517884283765085 -6.214501992265549 -7.783632894554249
VL40-all 0.01564684079201662 0.011553701899932127
RT-VL40-all -7.373621983306305 1.207038419396628 -6.5170276574487085 -9.762651764082259
*** budget = 50
VL50-1 0.033660264840085746 0.026185724703071907
RT-VL50-1 -9.908292613282454 2.48802055760121 -6.691010278885132 -13.917296042158783
VL50-2 0.004106415212196372 0.0034146275289423707
RT-VL50-2 -6.4169668148097845 0.22005521142075482 -6.016705638491989 -7.116738780383019
VL50-3 0.00951240066710876 0.010206842114146696
RT-VL50-3 -6.840110451268343 0.24829374080395528 -6.294817838626114 -7.217459464733848
VL50-4 0.008068839501068388 0.007532487495774762
RT-VL50-4 -6.2012944473098095 0.5127589402836756 -4.7279891091074955 -7.123878974544381
VL50-5 0.014367300158321808 0.01035317167675121
RT-VL50-5 -6.751154514118621 0.30999276580582513 -6.214501992265549 -7.783632894554249
VL50-all 0.013943044075756217 0.010390812631283414
RT-VL50-all -7.223563768157803 1.3618817225351876 -6.2012944473098095 -9.908292613282454
logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00021_21_dl_scale=0.5000,dr_cc=0.7000,seed=3_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s3/05.31_08.30.06/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00021_21_dl_scale=0.5000,dr_cc=0.7000,seed=3_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s3/05.31_08.30.06/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -8.51620281591303 4.742195888987125 -5.694579864411515 -44.06910774520085
LR -0.03892047694956313 0.07085408812875152 -7.222646845805869e-05 -0.9495794975546257
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.11304999094680823 0.04103936315613852
RT-VL10-1 -7.604604509806898 1.6672999020430912 -6.20193213278633 -20.000024138165212
VL10-2 0.019245019655079704 0.02255409615000745
RT-VL10-2 -7.062864281975174 1.602428897594367 -6.083600516028415 -20.000024138165212
VL10-3 0.013112040503176435 0.023136508400244483
RT-VL10-3 -15.17778043907599 6.200905563880537 -5.74121659583911 -21.227881552539465
VL10-4 0.02792015095872047 0.02684907371796799
RT-VL10-4 -6.579562722196048 1.930131057990529 -5.796953586573187 -29.82959492708897
VL10-5 0.07711817838396262 0.04904655799738647
RT-VL10-5 -6.709999306788467 1.100170339264767 -5.760254991321668 -14.537482617321263
VL10-all 0.05008907608954949 0.03874214085475864
RT-VL10-all -8.626962251968516 3.294584298540228 -6.579562722196048 -15.17778043907599
*** budget = 20
VL20-1 0.09233634006478598 0.03422168614192356
RT-VL20-1 -7.856571624389066 1.6243826399241887 -6.356672347893996 -13.100047134015554
VL20-2 0.008395159226168175 0.0071977076871171915
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VL20-3 0.0020378440296263322 0.0033652325312830116
RT-VL20-3 -18.2994938364637 4.129597818244547 -5.982685255559868 -20.00754980222063
VL20-4 0.014553926787829685 0.015257105881164634
RT-VL20-4 -6.764073799011036 2.6935175383478573 -5.843288555844811 -29.82959492708897
VL20-5 0.050526047453367466 0.033350839239201624
RT-VL20-5 -6.880058552105758 1.4371296520058277 -5.874455005090891 -14.537482617321263
VL20-all 0.03356986351235553 0.03385454409173502
RT-VL20-all -9.312120037188453 4.512432486404244 -6.760402373972704 -18.2994938364637
*** budget = 30
VL30-1 0.07929740890583722 0.032367165852197115
RT-VL30-1 -8.241712561005775 1.8379085006158107 -6.608684291814169 -13.100047134015554
VL30-2 0.006071462751193682 0.0058391526662149315
RT-VL30-2 -6.736747394340991 0.5064107937690927 -6.083600516028415 -8.364944966655132
VL30-3 0.0013343038767930416 0.0022018078540315565
RT-VL30-3 -18.428749363671283 4.2584231463719355 -6.09929375536802 -20.00754980222063
VL30-4 0.010553322151302285 0.013674867375697187
RT-VL30-4 -6.880911132716843 3.2888719592558 -5.843288555844811 -29.82959492708897
VL30-5 0.041245352016610184 0.026515466995501297
RT-VL30-5 -7.114167052482867 1.7781588694098789 -5.874455005090891 -14.537482617321263
VL30-all 0.02770036994034728 0.0293350346282294
RT-VL30-all -9.480457500843553 4.505359769050431 -6.736747394340991 -18.428749363671283
*** budget = 40
VL40-1 0.07359276667303101 0.03325267379225219
RT-VL40-1 -8.420976888830818 1.8852013985653449 -6.608684291814169 -13.100047134015554
VL40-2 0.00423407369973408 0.0037065187661628803
RT-VL40-2 -6.6666586544168895 0.4460165548024043 -6.083600516028415 -8.244041374812403
VL40-3 0.0008659209025780098 0.000172726041632086
RT-VL40-3 -18.556660491189014 4.1496650526626535 -6.09929375536802 -20.00754980222063
VL40-4 0.00704171859630125 0.008266125805011397
RT-VL40-4 -6.514747669388834 0.3973096784695116 -6.029006582003145 -8.2400864633731
VL40-5 0.03286284387705662 0.019451918121320376
RT-VL40-5 -7.253535109030072 1.9546334166936397 -5.874455005090891 -14.537482617321263
VL40-all 0.023719464749740193 0.02738999734976964
RT-VL40-all -9.482515762571126 4.586327960333398 -6.514747669388834 -18.556660491189014
*** budget = 50
VL50-1 0.0692781103322356 0.03354354794628516
RT-VL50-1 -8.682781071065113 2.0977782992562934 -6.608684291814169 -13.100047134015554
VL50-2 0.0032424336255943245 0.00214083003207536
RT-VL50-2 -6.678974652410102 0.48575702207166555 -6.083600516028415 -8.244041374812403
VL50-3 0.0008476144886916541 0.000179736149279044
RT-VL50-3 -18.675711863282718 3.977392800155454 -6.09929375536802 -20.00754980222063
VL50-4 0.004846413189623611 0.004809662350069377
RT-VL50-4 -6.44808510359037 0.27264243741063154 -6.0708076365461014 -7.336995888872302
VL50-5 0.028508657840130687 0.016185299035307826
RT-VL50-5 -7.531059371267464 2.188270542892045 -5.874455005090891 -14.537482617321263
VL50-all 0.021344645895255178 0.025957465777784502
RT-VL50-all -9.603322412323156 4.6034172767087 -6.44808510359037 -18.675711863282718
logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00013_13_dl_scale=0.5000,dr_cc=0.7000,seed=2_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s2/05.30_19.40.42/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00013_13_dl_scale=0.5000,dr_cc=0.7000,seed=2_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s2/05.30_19.40.42/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.2212014912988485 3.572064974989435 -4.350984806391587 -49.731010829865454
LR -0.03088052452526033 0.0553390213334963 -2.5093761370831302e-05 -0.7018128621517048
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.09315715600945915 0.07083064367222326
RT-VL10-1 -7.174546334958907 1.6672313418904863 -4.784767352326467 -18.46279374672194
VL10-2 0.03929331083388789 0.038290305243334814
RT-VL10-2 -6.164589470476615 0.8688918268431405 -4.784767352326467 -11.709445509040314
VL10-3 0.027017608271917244 0.028128961575020168
RT-VL10-3 -8.658684565821064 4.899638247402292 -4.372101502986194 -21.1357870401173
VL10-4 0.02106532170035508 0.02172067328629856
RT-VL10-4 -5.973775234701008 0.993589380550753 -4.3700464237645384 -8.623476871809427
VL10-5 0.05819580556016928 0.04986424077638824
RT-VL10-5 -6.688548574010511 1.7398401542469824 -4.450449945158881 -19.12028668025622
VL10-all 0.04774584047515773 0.0260167034750156
RT-VL10-all -6.932028835993622 0.9598594197861569 -5.973775234701008 -8.658684565821064
*** budget = 20
VL20-1 0.05233295556581184 0.03736512445701562
RT-VL20-1 -7.389953922754611 1.5648741801050086 -5.84861199678279 -18.46279374672194
VL20-2 0.020164774792162995 0.014197301560788593
RT-VL20-2 -6.143408900572359 0.938201284485352 -4.784767352326467 -11.709445509040314
VL20-3 0.01180829891484117 0.013346629144784388
RT-VL20-3 -10.043228506305327 5.734454772134924 -4.756145677230537 -20.000024138165212
VL20-4 0.010372544496253229 0.009386789126459256
RT-VL20-4 -5.956703342120115 1.0417193007903593 -4.512024299839796 -8.623476871809427
VL20-5 0.03198981847301173 0.029894645395346253
RT-VL20-5 -6.8787370475587535 1.4677088742366176 -4.477502720074352 -10.487879883456838
VL20-all 0.0253336784484162 0.01553547006407201
RT-VL20-all -7.282406343862233 1.4731909597733521 -5.956703342120115 -10.043228506305327
*** budget = 30
VL30-1 0.03662259936384486 0.029385227177627198
RT-VL30-1 -7.711487862709964 1.7744335280722763 -5.766082018626447 -18.46279374672194
VL30-2 0.014614152164286125 0.01058154168349954
RT-VL30-2 -6.075950554173089 0.750525279647854 -4.784767352326467 -8.71415546456015
VL30-3 0.007955923698452258 0.011023835937371815
RT-VL30-3 -10.852951319882491 6.271570263837588 -4.756145677230537 -20.000024138165212
VL30-4 0.007260917288459592 0.007121926140519123
RT-VL30-4 -5.99210729858033 1.0707979253593523 -4.534834239499929 -8.623476871809427
VL30-5 0.022068403737329937 0.023870964541019155
RT-VL30-5 -6.959456788531212 1.4950123320480917 -4.556177906472158 -10.487879883456838
VL30-all 0.017704399250474553 0.010868224775222701
RT-VL30-all -7.5183907647754165 1.7820865722545511 -5.99210729858033 -10.852951319882491
*** budget = 40
VL40-1 0.030609512449832406 0.026381945557650176
RT-VL40-1 -7.705900156162156 0.8755551274860642 -6.375722980931878 -10.951872774186434
VL40-2 0.012062987065967427 0.008885602886270537
RT-VL40-2 -6.053467286859044 0.6169539812473498 -5.2658423342814 -8.241263617682687
VL40-3 0.004938200006077856 0.006589590813802064
RT-VL40-3 -12.136558558117954 6.459751853326987 -4.910211461817992 -20.000024138165212
VL40-4 0.005461681457063272 0.006489653460442302
RT-VL40-4 -5.898252322987203 1.0125142452276685 -4.534834239499929 -8.158988104373268
VL40-5 0.016305235341237245 0.0206472827364587
RT-VL40-5 -7.095760049104674 1.5061500791756195 -4.556177906472158 -10.07205257686795
VL40-all 0.013875523264035641 0.009379591284141961
RT-VL40-all -7.777987674646207 2.279260706886823 -5.898252322987203 -12.136558558117954
*** budget = 50
VL50-1 0.02332679472600319 0.01957083991949083
RT-VL50-1 -8.067837534404502 2.037142721560048 -6.375722980931878 -18.46279374672194
VL50-2 0.01121984781135364 0.008056079058264436
RT-VL50-2 -6.129773805417997 0.776915511367726 -4.784767352326467 -8.241263617682687
VL50-3 0.003942633503964107 0.005558014269451683
RT-VL50-3 -13.91487481232291 6.348095916869791 -5.261608126891877 -20.000024138165212
VL50-4 0.004349482218314549 0.0043067083082917864
RT-VL50-4 -6.016049448926027 1.0469456013468308 -4.5727422657545365 -8.158988104373268
VL50-5 0.012232644244635422 0.01286804600650582
RT-VL50-5 -6.959430261617198 1.4588452523219042 -4.684593937048575 -10.07205257686795
VL50-all 0.011014280500854182 0.007036368410925781
RT-VL50-all -8.217593172537727 2.941775083900751 -6.016049448926027 -13.91487481232291
logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00005_5_dl_scale=0.5000,dr_cc=0.7000,seed=1_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s1/05.30_19.40.43/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/igsn/run_2024-05-30_19-40-10/run_f3ead_00005_5_dl_scale=0.5000,dr_cc=0.7000,seed=1_2024-05-30_19-40-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.70_reg10_ds0.500_s1/05.30_19.40.43/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.4752318934974 4.157093592892588 -4.410776284866959 -45.721324923281664
LR -0.026222285549477146 0.06132378481310766 -3.2139728268543166e-05 -0.6507142727381318
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.057065257814810554 0.041238939300624965
RT-VL10-1 -8.703547659349873 4.169691254779832 -4.440931712311907 -41.497048131754035
VL10-2 0.02347846380201492 0.02104576321926781
RT-VL10-2 -6.855159444344849 2.8496269893755137 -5.225572297167366 -27.21328473954686
VL10-3 0.045839239469542936 0.037486915890846094
RT-VL10-3 -8.218348438861637 3.973437742221361 -5.732863474073936 -34.564837146569545
VL10-4 0.02426142142301825 0.01954614730960101
RT-VL10-4 -6.399508538357385 1.344421990283366 -4.484794717304663 -21.557166803106966
VL10-5 0.08087249124234275 0.04737487164779318
RT-VL10-5 -6.441700385095831 2.5716233149020145 -4.410776284866959 -31.409509821727166
VL10-all 0.046303374750345884 0.021530822696648987
RT-VL10-all -7.323652893201915 0.9545636502437633 -6.399508538357385 -8.703547659349873
*** budget = 20
VL20-1 0.033298381856156536 0.028027252950813437
RT-VL20-1 -9.497438323482154 3.3757800163928517 -4.440931712311907 -24.335271503438346
VL20-2 0.011808464997887229 0.009488776897415991
RT-VL20-2 -6.691548146009486 2.418025345587485 -5.580618749644609 -27.21328473954686
VL20-3 0.02375170465860483 0.02079194298208068
RT-VL20-3 -8.666068155441721 4.839708096053875 -5.73452983708218 -34.564837146569545
VL20-4 0.013924946568761533 0.01203340847976072
RT-VL20-4 -6.342865999255467 0.4802795469710852 -5.064761700546083 -7.105665210678911
VL20-5 0.05579157975657495 0.040155426101918974
RT-VL20-5 -6.842310021832244 3.060272904394428 -4.568708080904182 -31.409509821727166
VL20-all 0.027715015567597014 0.01598974435787349
RT-VL20-all -7.608046129204214 1.242273284937935 -6.342865999255467 -9.497438323482154
*** budget = 30
VL30-1 0.020717387237035912 0.015149000166830654
RT-VL30-1 -10.292100861558808 3.461037010425805 -5.303935524988596 -24.335271503438346
VL30-2 0.008629079935388622 0.007056743204093565
RT-VL30-2 -6.441748019231804 0.39680766129865097 -5.580618749644609 -7.002007161172987
VL30-3 0.015094002502329628 0.011777315934686896
RT-VL30-3 -8.969163369489364 5.40273763428769 -5.73452983708218 -34.564837146569545
VL30-4 0.010035823038806083 0.009309870623560321
RT-VL30-4 -6.337146924840714 0.4725357325405281 -5.064761700546083 -7.0224760277192395
VL30-5 0.03994392944891138 0.028762752317226036
RT-VL30-5 -7.168108346819809 3.67609958719253 -4.568708080904182 -31.409509821727166
VL30-all 0.018884044432494328 0.011355171854663402
RT-VL30-all -7.8416535043881 1.5461506550605817 -6.337146924840714 -10.292100861558808
*** budget = 40
VL40-1 0.01954973977267701 0.015870194269294237
RT-VL40-1 -10.249999744685644 3.3193752768018383 -4.946188715530734 -24.335271503438346
VL40-2 0.006885534532107759 0.0065124318660166195
RT-VL40-2 -6.469434725740051 0.3786858157289452 -5.580618749644609 -6.949246656135372
VL40-3 0.011672575115824563 0.010571752620865084
RT-VL40-3 -8.56761703446305 5.180207989072138 -5.73452983708218 -34.564837146569545
VL40-4 0.007329298758624113 0.00601654587689673
RT-VL40-4 -6.31020940615261 0.42369250545730247 -5.064761700546083 -6.955256218902903
VL40-5 0.0300468013430562 0.02476053933911876
RT-VL40-5 -6.778569507738839 1.2368615145024793 -5.200835389901512 -12.86932728582682
VL40-all 0.01509678990445793 0.008750034113674565
RT-VL40-all -7.675166083756038 1.5196721580944381 -6.31020940615261 -10.249999744685644
*** budget = 50
VL50-1 0.015698583033244673 0.010755600444902987
RT-VL50-1 -10.307765851814942 3.446459959311153 -5.303935524988596 -24.335271503438346
VL50-2 0.005137887022663704 0.004994882980512891
RT-VL50-2 -6.431296972820699 0.39955164775378216 -5.580618749644609 -6.817692983130317
VL50-3 0.010691698099842923 0.009860967659681112
RT-VL50-3 -8.867814210922486 5.705613724859348 -5.92998279897839 -34.564837146569545
VL50-4 0.005699037850862221 0.004852874451172175
RT-VL50-4 -6.387958844338132 0.35944976683268987 -5.198607512710103 -6.955256218902903
VL50-5 0.02695473008891585 0.022341119906403327
RT-VL50-5 -7.000127814561204 1.2934838181983106 -5.60906643285865 -12.86932728582682
VL50-all 0.012836387219105874 0.008029048797525905
RT-VL50-all -7.798992738891494 1.5450641021416391 -6.387958844338132 -10.307765851814942
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00018_18_clip_discriminator=0,dr_cc=0.7000,seed=5_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s5/04.09_10.37.15/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00018_18_clip_discriminator=0,dr_cc=0.7000,seed=5_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s5/04.09_10.37.15/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -4.950062526880093 2.540737597522177 -2.571925695801297 -26.48268022347472
LR -0.020127739973579986 0.03073668326461691 -4.512385626997961e-05 -0.2729486889485542
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.04130747548640615 0.03492838932669081
RT-VL10-1 -5.43995788368836 1.4130353881246591 -3.1959426114896416 -14.933441071059896
VL10-2 0.014659301149146516 0.013688008123153737
RT-VL10-2 -4.304215005697689 2.023263250772754 -3.2222432328621173 -25.317080916428164
VL10-3 0.02130144819229547 0.020677617553825217
RT-VL10-3 -6.07080030946107 4.195401257220708 -3.5945724962193797 -21.50063121727516
VL10-4 0.10435759203718394 0.05930641597321975
RT-VL10-4 -4.757365159582438 2.130195061081725 -3.3995284432051855 -20.302337012346033
VL10-5 0.25928737887125136 0.06783019981079397
RT-VL10-5 -4.770578823314759 2.12993519373287 -3.3995284432051855 -20.302337012346033
VL10-all 0.08818263914725669 0.09122994788903169
RT-VL10-all -5.068583436348864 0.6184303200598651 -4.304215005697689 -6.07080030946107
*** budget = 20
VL20-1 0.025348026194162165 0.02613043678469473
RT-VL20-1 -5.614705841624886 0.8793336484519422 -3.5676083984332054 -8.700056630434075
VL20-2 0.0070658912533247565 0.006419979474548507
RT-VL20-2 -4.096657029012092 0.3917856594940166 -3.247197800774128 -5.867009965624753
VL20-3 0.011286113230889227 0.011299384035495566
RT-VL20-3 -6.448669622201479 4.87896617716205 -4.077210973384197 -21.50063121727516
VL20-4 0.06920694127530277 0.04495681405554
RT-VL20-4 -4.315426301379523 0.5697251755231791 -3.3995284432051855 -6.159091742543729
VL20-5 0.21931303557771717 0.05849796131927777
RT-VL20-5 -4.360021327724159 0.5922089368442187 -3.3995284432051855 -6.159091742543729
VL20-all 0.06644400150627922 0.07954069048879103
RT-VL20-all -4.967096024388427 0.9127251460331343 -4.096657029012092 -6.448669622201479
*** budget = 30
VL30-1 0.016620632047046204 0.01683545905112275
RT-VL30-1 -5.835667227728084 0.6945109746004717 -4.28348134096562 -7.701171124368813
VL30-2 0.0050388434348262925 0.004955036035110101
RT-VL30-2 -4.039746184995642 0.41759239339033394 -3.247197800774128 -5.867009965624753
VL30-3 0.007232523655043665 0.006685376585558329
RT-VL30-3 -6.350829336815744 4.71806482485966 -4.077210973384197 -21.50063121727516
VL30-4 0.05195766153871301 0.02930252509924447
RT-VL30-4 -4.273205868382534 0.6127661340257397 -3.4497661221255775 -6.159091742543729
VL30-5 0.19780841328691667 0.048915334375970496
RT-VL30-5 -4.348922139326609 0.645309920510589 -3.4497661221255775 -6.159091742543729
VL30-all 0.05573161479250917 0.07300910528691708
RT-VL30-all -4.969674151449723 0.9373047323994886 -4.039746184995642 -6.350829336815744
*** budget = 40
VL40-1 0.011223396210220633 0.010263345049702407
RT-VL40-1 -6.032760321358068 0.607100202028912 -4.711856454421218 -7.701171124368813
VL40-2 0.003826336438743014 0.003970665377675203
RT-VL40-2 -4.024933130707309 0.42130264203423673 -3.247197800774128 -5.867009965624753
VL40-3 0.004922858218245854 0.004256612708147457
RT-VL40-3 -6.474611941136157 4.922908634489782 -4.077210973384197 -21.50063121727516
VL40-4 0.04302750572981147 0.027597614408171097
RT-VL40-4 -4.319713034256529 0.670109262456014 -3.4497661221255775 -6.159091742543729
VL40-5 0.1839726014709065 0.048934165025795694
RT-VL40-5 -4.416483569727708 0.7118590394293445 -3.4497661221255775 -6.159091742543729
VL40-all 0.04939453961358549 0.06879392651852723
RT-VL40-all -5.0537003994371545 0.998067409904245 -4.024933130707309 -6.474611941136157
*** budget = 50
VL50-1 0.009068927446120003 0.009219615946398968
RT-VL50-1 -5.992600764448146 0.551795708694896 -4.451843669799521 -7.701171124368813
VL50-2 0.0029079540953552475 0.0033672139019070607
RT-VL50-2 -4.080582024592598 0.4565093341843878 -3.247197800774128 -5.867009965624753
VL50-3 0.004239617559636444 0.003696893417925583
RT-VL50-3 -5.77866988636085 4.018747378137694 -4.077210973384197 -21.50063121727516
VL50-4 0.03681239931505727 0.021887080664426354
RT-VL50-4 -4.200103823863243 0.5827409204682505 -3.4497661221255775 -6.159091742543729
VL50-5 0.17432683277804442 0.0466125863817749
RT-VL50-5 -4.3451879435533876 0.6523184336600405 -3.4497661221255775 -6.159091742543729
VL50-all 0.04547114623884267 0.0655980909287459
RT-VL50-all -4.879428888563645 0.8285937774701868 -4.080582024592598 -5.992600764448146
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00014_14_clip_discriminator=0,dr_cc=0.7000,seed=4_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s4/04.09_10.37.18/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00014_14_clip_discriminator=0,dr_cc=0.7000,seed=4_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s4/04.09_10.37.18/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.993035988189025 0.6331614653817216 -7.887925843595715 -23.855282558139024
LR -0.1998674129851607 0.014516995629776087 -0.004947100534610893 -0.39577011249298
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.3177664623028289 0.007749038033206129
RT-VL10-1 -20.056454080117884 0.28939557218963563 -19.98419285049374 -22.11520207688625
VL10-2 0.15776646230282892 0.0077490380332061294
RT-VL10-2 -20.056454080117884 0.28939557218963563 -19.98419285049374 -22.11520207688625
VL10-3 0.0008751903991951826 0.00013308081962713542
RT-VL10-3 -20.060429763166646 0.3282071031773558 -20.000024138165212 -22.022197419091903
VL10-4 0.15345703983013156 0.022878000961875627
RT-VL10-4 -19.92457565524491 1.6661967112557634 -7.887925843595715 -23.100955036196062
VL10-5 0.3021065807022809 0.06117119322728749
RT-VL10-5 -19.815557272926 1.9425048394353601 -7.887925843595715 -23.855282558139024
VL10-all 0.18639434710745312 0.11573221950625658
RT-VL10-all -19.982694170314666 0.09822054290934178 -19.815557272926 -20.060429763166646
*** budget = 20
VL20-1 0.3164391744150833 0.010796720807029078
RT-VL20-1 -20.112660331489263 0.4014693092208327 -19.98419285049374 -22.11520207688625
VL20-2 0.1564391744150833 0.01079672080702908
RT-VL20-2 -20.112660331489263 0.4014693092208327 -19.98419285049374 -22.11520207688625
VL20-3 0.0008500748948868253 0.000184822704211182
RT-VL20-3 -20.120835388168082 0.45622595946363537 -20.000024138165212 -22.022197419091903
VL20-4 0.1460271181367227 0.030600411858658385
RT-VL20-4 -19.848810625985315 2.3539190442673137 -7.887925843595715 -23.100955036196062
VL20-5 0.2836926443560384 0.08243629920015018
RT-VL20-5 -19.58942811604041 2.7051615928763924 -7.887925843595715 -23.855282558139024
VL20-all 0.1806896372435629 0.11240053352229745
RT-VL20-all -19.95687895863447 0.21076824679591175 -19.58942811604041 -20.120835388168082
*** budget = 30
VL30-1 0.31513472575945745 0.013028276363503469
RT-VL30-1 -20.16512753988029 0.4825968228455839 -19.98419285049374 -22.11520207688625
VL30-2 0.15513472575945741 0.013028276363503469
RT-VL30-2 -20.16512753988029 0.4825968228455839 -19.98419285049374 -22.11520207688625
VL30-3 0.0008389154610007732 0.0002037559761469028
RT-VL30-3 -20.15556795542883 0.5262670924035341 -20.000024138165212 -22.022197419091903
VL30-4 0.1398542796504424 0.035424807333846335
RT-VL30-4 -19.58471614699374 2.794198288262154 -7.887925843595715 -23.100955036196062
VL30-5 0.26580290226689046 0.09597431874207117
RT-VL30-5 -19.257661000036993 3.2411832927321305 -7.887925843595715 -23.855282558139024
VL30-all 0.1753531097794497 0.10940221125601512
RT-VL30-all -19.865640036444027 0.37735936930468134 -19.257661000036993 -20.16512753988029
*** budget = 40
VL40-1 0.31425419863383386 0.014729587738218083
RT-VL40-1 -20.22353303053482 0.549196907177315 -19.98419285049374 -22.11520207688625
VL40-2 0.15425419863383377 0.014729587738218081
RT-VL40-2 -20.22353303053482 0.549196907177315 -19.98419285049374 -22.11520207688625
VL40-3 0.0007984862914426319 0.00025296118335841613
RT-VL40-3 -20.24491180708996 0.6257195298582108 -20.000024138165212 -22.022197419091903
VL40-4 0.13418659792300522 0.03809971783800505
RT-VL40-4 -19.36353594558364 3.2014826656301483 -7.887925843595715 -23.100955036196062
VL40-5 0.24817122308036751 0.10550363796466186
RT-VL40-5 -18.921569530777226 3.694424218383007 -7.887925843595715 -23.855282558139024
VL40-all 0.1703329409124966 0.10684488004894992
RT-VL40-all -19.79541666890409 0.5511339034087649 -18.921569530777226 -20.24491180708996
*** budget = 50
VL50-1 0.3130807995943885 0.01613502367060292
RT-VL50-1 -20.282037406070593 0.5959353277832112 -19.98419285049374 -22.11520207688625
VL50-2 0.15308079959438844 0.01613502367060292
RT-VL50-2 -20.282037406070593 0.5959353277832112 -19.98419285049374 -22.11520207688625
VL50-3 0.0007979884993322621 0.00025496396323289013
RT-VL50-3 -20.259263833604575 0.6593276876497148 -20.000024138165212 -22.022197419091903
VL50-4 0.1259805044873631 0.04012110812376123
RT-VL50-4 -19.299594625827506 3.575037679830165 -7.887925843595715 -23.100955036196062
VL50-5 0.2320636560983182 0.11127902511355624
RT-VL50-5 -18.642318605003297 4.028959893190292 -7.887925843595715 -23.855282558139024
VL50-all 0.16500074965475814 0.10493078301834241
RT-VL50-all -19.753050375315315 0.6716031276120985 -18.642318605003297 -20.282037406070593
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00010_10_clip_discriminator=0,dr_cc=0.7000,seed=3_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s3/04.09_10.37.14/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00010_10_clip_discriminator=0,dr_cc=0.7000,seed=3_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s3/04.09_10.37.14/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.585346850338023 2.8373559539841926 -6.092142585571217 -62.4805862068344
LR -0.19333339384744966 0.043532333678010564 -0.000623066456435506 -0.7925879502227726
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.3092412997986085 0.04085522064035378
RT-VL10-1 -20.873540135736313 4.257120261767825 -17.628893920902172 -62.4805862068344
VL10-2 0.1535426711212952 0.02250302171640255
RT-VL10-2 -20.59033638861185 2.550878321351108 -17.628893920902172 -38.027915689098954
VL10-3 0.0008890227807316113 6.659232119508838e-05
RT-VL10-3 -20.00830588304383 0.5362534933916069 -14.650563352092448 -23.041711727547806
VL10-4 0.13589603496892455 0.042822641829631254
RT-VL10-4 -17.556502021992948 5.150901625133043 -6.092142585571217 -24.722230370522283
VL10-5 0.22325303454556025 0.1230170342294052
RT-VL10-5 -15.78035879843086 5.94955363333527 -6.092142585571217 -24.722230370522283
VL10-all 0.16456441264302402 0.10208696677740856
RT-VL10-all -18.96180864556316 1.9747651103117065 -15.78035879843086 -20.873540135736313
*** budget = 20
VL20-1 0.30116665367340395 0.05517018137086264
RT-VL20-1 -21.52621060292723 5.686228153128733 -17.628893920902172 -62.4805862068344
VL20-2 0.14976939631877711 0.028638215599293148
RT-VL20-2 -20.959803108678305 3.111262018348288 -17.628893920902172 -38.027915689098954
VL20-3 0.0008777396579596915 9.281409788026387e-05
RT-VL20-3 -20.01658762792245 0.7582865182455343 -14.650563352092448 -23.041711727547806
VL20-4 0.11431526484045267 0.050733954678400445
RT-VL20-4 -15.435341278929918 6.132349616523318 -6.092142585571217 -24.381369196033727
VL20-5 0.15237023575208036 0.12459012060722321
RT-VL20-5 -12.564598518054803 6.040517843728546 -6.092142585571217 -24.381369196033727
VL20-all 0.14369985804853475 0.09609309890754518
RT-VL20-all -18.10050822730254 3.5023356614163483 -12.564598518054803 -21.52621060292723
*** budget = 30
VL30-1 0.2931741190552578 0.06602558932229698
RT-VL30-1 -22.20602096553395 6.848290352330799 -17.628893920902172 -62.4805862068344
VL30-2 0.1458939049794887 0.0342462629349344
RT-VL30-2 -21.269243994947548 3.703426643178514 -17.628893920902172 -38.027915689098954
VL30-3 0.0008664565351877717 0.00011198106344169162
RT-VL30-3 -20.024869372801078 0.9285967390286802 -14.650563352092448 -23.041711727547806
VL30-4 0.09689366555299211 0.05166015829821984
RT-VL30-4 -14.160610640389713 6.367276940053933 -6.092142585571217 -24.722230370522283
VL30-5 0.11141584005113873 0.1092574668515056
RT-VL30-5 -10.852540504883931 5.331898021088356 -6.092142585571217 -24.722230370522283
VL30-all 0.12964879723481304 0.09486270611504359
RT-VL30-all -17.702657095711242 4.424108690194641 -10.852540504883931 -22.20602096553395
*** budget = 40
VL40-1 0.28333694072208554 0.07492712402927758
RT-VL40-1 -22.20953198944475 4.746858540471122 -17.628893920902172 -38.027915689098954
VL40-2 0.13826143127463766 0.04135035046853402
RT-VL40-2 -22.20953198944475 4.746858540471122 -17.628893920902172 -38.027915689098954
VL40-3 0.000854563513887649 0.00012806874901605418
RT-VL40-3 -20.033598779565033 1.0793360193514723 -14.650563352092448 -23.041711727547806
VL40-4 0.08594178442721942 0.05131699982646924
RT-VL40-4 -13.02329225570374 6.014959699781681 -6.092142585571217 -24.381369196033727
VL40-5 0.0894594824575015 0.09703381283655223
RT-VL40-5 -9.939524466354104 4.284193605022315 -6.092142585571217 -21.428982744078272
VL40-all 0.11957084047906634 0.09305894420463649
RT-VL40-all -17.483095896102473 5.059224835369103 -9.939524466354104 -22.20953198944475
*** budget = 50
VL50-1 0.27553658166750405 0.0806375541301507
RT-VL50-1 -23.641102141137935 8.544955629980333 -17.628893920902172 -62.4805862068344
VL50-2 0.13631837802991195 0.04178379182764296
RT-VL50-2 -22.108603697620044 4.595700215659692 -17.628893920902172 -38.027915689098954
VL50-3 0.0008438902896439597 0.00014009464308469433
RT-VL50-3 -20.041432862558324 1.1985271377439974 -14.650563352092448 -23.041711727547806
VL50-4 0.07637423598270607 0.04986455913098311
RT-VL50-4 -12.585581502985317 5.644780107081958 -6.092142585571217 -23.72550790270697
VL50-5 0.07043179386771244 0.07665870118656644
RT-VL50-5 -9.121728991286023 3.3218989648903627 -6.092142585571217 -20.821141706679597
VL50-all 0.11190097596749571 0.0923995017015956
RT-VL50-all -17.49968983911753 5.652577789340997 -9.121728991286023 -23.641102141137935
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00006_6_clip_discriminator=0,dr_cc=0.7000,seed=2_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s2/04.09_10.37.14/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00006_6_clip_discriminator=0,dr_cc=0.7000,seed=2_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s2/04.09_10.37.14/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
LR -0.2000004781111453 0.0 -0.2000004781111453 -0.2000004781111453
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.3190996940964965 0.0
RT-VL10-1 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-2 0.15909969409649652 2.7755575615628914e-17
RT-VL10-2 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-3 0.0009003059035035377 1.5543209467896877e-17
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.16090030590350357 1.570092458683775e-17
RT-VL10-4 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-5 0.32090030590350355 5.551115123125783e-17
RT-VL10-5 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-all 0.19218006118070075 0.11944720438462349
RT-VL10-all -20.00002413816521 0.0 -20.00002413816521 -20.00002413816521
*** budget = 20
VL20-1 0.3190996940964965 0.0
RT-VL20-1 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-2 0.15909969409649652 2.7755575615628914e-17
RT-VL20-2 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-3 0.0009003059035035354 2.175583972041798e-17
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.16090030590350357 2.2204460492503132e-17
RT-VL20-4 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-5 0.32090030590350355 5.551115123125783e-17
RT-VL20-5 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-all 0.19218006118070075 0.11944720438462349
RT-VL20-all -20.00002413816521 0.0 -20.00002413816521 -20.00002413816521
*** budget = 30
VL30-1 0.3190996940964965 0.0
RT-VL30-1 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-2 0.1590996940964965 0.0
RT-VL30-2 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-3 0.0009003059035035333 2.6366379131065376e-17
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.16090030590350357 2.7194799110210366e-17
RT-VL30-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-5 0.3209003059035036 2.7194799110210366e-17
RT-VL30-5 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-all 0.19218006118070075 0.11944720438462351
RT-VL30-all -20.000024138165216 0.0 -20.000024138165216 -20.000024138165216
*** budget = 40
VL40-1 0.31909969409649663 1.1102230246251565e-16
RT-VL40-1 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-2 0.15909969409649644 5.551115123125783e-17
RT-VL40-2 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-3 0.0009003059035035309 3.0304602627846013e-17
RT-VL40-3 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-4 0.16090030590350352 5.551115123125783e-17
RT-VL40-4 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-5 0.32090030590350355 5.551115123125783e-17
RT-VL40-5 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-all 0.19218006118070075 0.11944720438462353
RT-VL40-all -20.00002413816522 0.0 -20.00002413816522 -20.00002413816522
*** budget = 50
VL50-1 0.3190996940964966 5.551115123125783e-17
RT-VL50-1 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-2 0.15909969409649646 2.7755575615628914e-17
RT-VL50-2 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-3 0.0009003059035035288 3.33067189731743e-17
RT-VL50-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-4 0.16090030590350352 5.551115123125783e-17
RT-VL50-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-5 0.32090030590350355 5.551115123125783e-17
RT-VL50-5 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-all 0.19218006118070075 0.11944720438462351
RT-VL50-all -20.000024138165216 0.0 -20.000024138165216 -20.000024138165216
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00002_2_clip_discriminator=0,dr_cc=0.7000,seed=1_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s1/04.09_10.37.14/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00002_2_clip_discriminator=0,dr_cc=0.7000,seed=1_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.70_reg00_ds0.001_zt=p_g1_s1/04.09_10.37.14/models/ckpt_policy_T10000000.pt
Error(s) in loading state_dict for PairDiscriminator:
	Missing key(s) in state_dict: "reward_offset". 
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -20.043484104375757 0.44076221857147463 -18.07289515962984 -29.54874134028533
LR -0.20056724511321447 0.005153593350714029 -0.17353891117990217 -0.3029206246774432
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.30661456855749664 0.034898493651629614
RT-VL10-1 -20.304884990789983 1.0595744515141379 -18.07289515962984 -27.501294448398326
VL10-2 0.14771016452593688 0.029923968122131977
RT-VL10-2 -20.304884990789983 1.0595744515141379 -18.07289515962984 -27.501294448398326
VL10-3 0.0008902210984965323 8.6782163157911e-05
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VL10-4 0.16085370803699892 0.00032680981209218176
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VL10-5 0.32085370803699886 0.0003268098120921818
RT-VL10-5 -20.058553012121994 0.35896133994238777 -20.000024138165212 -22.710622339137462
VL10-all 0.18738447405118558 0.1175481949012659
RT-VL10-all -20.150282557909524 0.1268419830903694 -20.024536783723665 -20.304884990789983
*** budget = 20
VL20-1 0.2966746899080169 0.04463552690631279
RT-VL20-1 -20.503795664518147 1.3095269966924763 -18.07289515962984 -27.501294448398326
VL20-2 0.13886588184489734 0.03726349397326755
RT-VL20-2 -20.503795664518147 1.3095269966924763 -18.07289515962984 -27.501294448398326
VL20-3 0.0008801362934895248 0.00012189701022040632
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VL20-4 0.16080711017049423 0.0004574566473818942
RT-VL20-4 -20.11708188607878 0.5008544988049624 -20.000024138165212 -22.710622339137462
VL20-5 0.3208071101704943 0.00045745664738189426
RT-VL20-5 -20.11708188607878 0.5008544988049624 -20.000024138165212 -22.710622339137462
VL20-all 0.18360698567747846 0.11620575035853804
RT-VL20-all -20.258160906095192 0.20209258205264993 -20.049049429282118 -20.503795664518147
*** budget = 30
VL30-1 0.28664083940683627 0.051295806765202
RT-VL30-1 -20.82510961777165 1.5949231390327372 -19.070573249944065 -27.501294448398326
VL30-2 0.12992762731215698 0.04228443754378518
RT-VL30-2 -20.82510961777165 1.5949231390327372 -19.070573249944065 -27.501294448398326
VL30-3 0.0008700514884825172 0.0001482673658460373
RT-VL30-3 -20.07356207484058 0.36064434428606174 -20.000024138165212 -21.921609179687575
VL30-4 0.1607605123039896 0.0005544238386491573
RT-VL30-4 -20.175610760035575 0.6049842611174332 -20.000024138165212 -22.710622339137462
VL30-5 0.32076051230398966 0.0005544238386491573
RT-VL30-5 -20.175610760035575 0.6049842611174332 -20.000024138165212 -22.710622339137462
VL30-all 0.17979190856309102 0.11502218289563086
RT-VL30-all -20.415000566091003 0.3369195958488233 -20.07356207484058 -20.82510961777165
*** budget = 40
VL40-1 0.27780696263827553 0.05627192385157622
RT-VL40-1 -20.96985203264192 1.6777243948224387 -19.070573249944065 -27.501294448398326
VL40-2 0.12224856791573602 0.045829002853709624
RT-VL40-2 -20.96985203264192 1.6777243948224387 -19.070573249944065 -27.501294448398326
VL40-3 0.0008594215588805363 0.00017109192042267526
RT-VL40-3 -20.099399728267063 0.41616702621437657 -20.000024138165212 -21.921609179687575
VL40-4 0.16071139563389006 0.0006372656927686347
RT-VL40-4 -20.23730335690895 0.6927945383191665 -20.000024138165212 -22.710622339137462
VL40-5 0.3207113956338901 0.0006372656927686348
RT-VL40-5 -20.23730335690895 0.6927945383191665 -20.000024138165212 -22.710622339137462
VL40-all 0.17646754867613446 0.11410708369805911
RT-VL40-all -20.502742101473764 0.38470349617546856 -20.099399728267063 -20.96985203264192
*** budget = 50
VL50-1 0.2677796805379256 0.05857983687162043
RT-VL50-1 -21.029323921785956 1.7157040532951238 -19.070573249944065 -27.501294448398326
VL50-2 0.11325766038012682 0.047124931723906055
RT-VL50-2 -21.029323921785956 1.7157040532951238 -19.070573249944065 -27.501294448398326
VL50-3 0.0008498818784685021 0.00018873699523740467
RT-VL50-3 -20.12258736595749 0.45909171979732066 -20.000024138165212 -21.921609179687575
VL50-4 0.16066731657098024 0.000700425615710134
RT-VL50-4 -20.292668507949152 0.7587843146330735 -20.000024138165212 -22.710622339137462
VL50-5 0.3206673165709803 0.000700425615710134
RT-VL50-5 -20.292668507949152 0.7587843146330735 -20.000024138165212 -22.710622339137462
VL50-all 0.1726443711876963 0.11326526145289084
RT-VL50-all -20.55331444508554 0.39359076960792694 -20.12258736595749 -21.029323921785956
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00019_19_clip_discriminator=10,dr_cc=0.7000,seed=5_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s5/04.09_10.37.15/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00019_19_clip_discriminator=10,dr_cc=0.7000,seed=5_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s5/04.09_10.37.15/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00019_19_clip_discriminator=10,dr_cc=0.7000,seed=5_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s5/04.09_10.37.15/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -5.722156151039216 3.3899378278850345 -3.148621460949119 -42.19528228757558
LR -0.02324226359909128 0.0413610248930295 -4.101632723618742e-05 -0.48184858522830154
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.026337603479791014 0.023098985133182977
RT-VL10-1 -5.362401741318677 2.174960683651439 -3.3678728430697173 -15.345168489608637
VL10-2 0.013603243548140445 0.012066617765943899
RT-VL10-2 -4.8604415523226345 0.8476689131019037 -3.804041326575661 -8.979886750880983
VL10-3 0.031128989998293152 0.029854232258129873
RT-VL10-3 -8.229697913547877 6.16514613532969 -4.184850889917087 -29.93324654869107
VL10-4 0.12631170768384573 0.07081725830024306
RT-VL10-4 -7.203273743981104 4.634141198133834 -4.169328785362692 -29.93324654869107
VL10-5 0.2810802986558383 0.07943105720425525
RT-VL10-5 -7.203273743981104 4.634141198133834 -4.169328785362692 -29.93324654869107
VL10-all 0.09569236867318172 0.10101818280829095
RT-VL10-all -6.57181773903028 1.259963138618441 -4.8604415523226345 -8.229697913547877
*** budget = 20
VL20-1 0.013675315164783016 0.013438830030081648
RT-VL20-1 -5.482995269588812 2.2831042018754055 -3.3843129720532863 -15.345168489608637
VL20-2 0.0062134795597272715 0.005489810946529473
RT-VL20-2 -4.938171306449129 0.8994190082668339 -3.9301279117595502 -8.842036305123603
VL20-3 0.012891439719780861 0.01428445566450656
RT-VL20-3 -9.415062145718519 6.785115591122639 -4.303756434643123 -25.24776094804459
VL20-4 0.08776672558837822 0.05630557986171679
RT-VL20-4 -6.960448394515439 3.1851819374590837 -4.169328785362692 -20.000024138165212
VL20-5 0.23790675155262467 0.07024401991192583
RT-VL20-5 -6.967092583012804 3.1879084122705494 -4.169328785362692 -20.000024138165212
VL20-all 0.07169074231705881 0.08831377998491217
RT-VL20-all -6.75275393985694 1.5544748462184907 -4.938171306449129 -9.415062145718519
*** budget = 30
VL30-1 0.009225276780920187 0.010421813941501082
RT-VL30-1 -5.3870802595455265 2.3486978718224782 -3.3843129720532863 -15.345168489608637
VL30-2 0.004688486102328049 0.004754941017398598
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VL30-3 0.006714508944998063 0.006689522338943693
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VL30-4 0.0653614957967732 0.0455484361960816
RT-VL30-4 -6.605321669675948 2.5128362181591144 -4.169328785362692 -19.922401747274957
VL30-5 0.21027764399573434 0.06292781075788444
RT-VL30-5 -6.663559926387477 2.522785681445642 -4.169328785362692 -19.922401747274957
VL30-all 0.05925348232415077 0.07884936159980614
RT-VL30-all -6.724220770043668 1.8085164450596494 -4.894356646922787 -10.070785347686604
*** budget = 40
VL40-1 0.006313455126955693 0.008762673532383455
RT-VL40-1 -5.5072797332757455 2.540815276064938 -3.3843129720532863 -15.345168489608637
VL40-2 0.003422254588357624 0.0033405198654658263
RT-VL40-2 -4.883500952464721 0.9870550780523721 -3.9301279117595502 -8.842036305123603
VL40-3 0.004587836037069092 0.005643939136997275
RT-VL40-3 -10.786893756066583 7.25283062293633 -4.430178436260144 -20.88336370624039
VL40-4 0.050157280004598744 0.03853998923570901
RT-VL40-4 -6.912544341824687 2.8094703082289216 -4.169328785362692 -20.062678107272177
VL40-5 0.19107144401693904 0.05851442242474775
RT-VL40-5 -6.921220307105008 2.8139234037078538 -4.169328785362692 -20.062678107272177
VL40-all 0.051110453954784044 0.07215992893631222
RT-VL40-all -7.002287818147349 2.0524217351335605 -4.883500952464721 -10.786893756066583
*** budget = 50
VL50-1 0.0038219188972034744 0.0037048418392483843
RT-VL50-1 -5.636346562055138 2.774806847688151 -3.3843129720532863 -15.345168489608637
VL50-2 0.002541506128230453 0.002372235185601227
RT-VL50-2 -4.685268394384566 0.6970387313457382 -4.047688170767484 -6.503512825367791
VL50-3 0.003080312882538679 0.0031916461564059665
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VL50-4 0.0452985803471317 0.03128738532108169
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VL50-5 0.18551093572097585 0.05448362500825915
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VL50-all 0.04805065079521603 0.0706434683310914
RT-VL50-all -7.083675171081924 2.1832297960967306 -4.685268394384566 -11.080414232268135
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00015_15_clip_discriminator=10,dr_cc=0.7000,seed=4_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s4/04.09_10.37.15/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00015_15_clip_discriminator=10,dr_cc=0.7000,seed=4_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s4/04.09_10.37.15/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00015_15_clip_discriminator=10,dr_cc=0.7000,seed=4_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s4/04.09_10.37.15/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.847674592845934 2.672502101238426 -4.979941338048793 -47.78076682622673
LR -0.1979336634553365 0.03738530296095592 -0.00033724024073022996 -0.5336125920967626
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.2812569414820128 0.055948190785864736
RT-VL10-1 -18.37653970387718 5.457868323319164 -7.4104632126924335 -43.23278096865015
VL10-2 0.12383843559216944 0.04964127749580122
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VL10-3 0.0008683717534344027 0.00011211578758163929
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VL10-4 0.13368000006971986 0.04260747653053594
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VL10-all 0.16645566924481656 0.10900466061940915
RT-VL10-all -19.261497154789044 0.782812510838423 -18.272053076631867 -20.18148216178575
*** budget = 20
VL20-1 0.25362513180667906 0.0643718568831513
RT-VL20-1 -16.829590661168954 6.530849213422176 -7.631675250761605 -43.23278096865015
VL20-2 0.09878812002699225 0.05553238612764607
RT-VL20-2 -16.620617406678317 5.903256473702913 -7.631675250761605 -28.333181444981612
VL20-3 0.0008364376033652654 0.00015198789349427203
RT-VL20-3 -20.362940185406295 1.1051580731366275 -20.000024138165212 -27.315851209400442
VL20-4 0.1115970517491325 0.048813765725546104
RT-VL20-4 -18.746088283870677 6.882403684959388 -4.979941338048793 -33.21480583100539
VL20-5 0.2695062462631853 0.05333917615368338
RT-VL20-5 -18.746088283870677 6.882403684959388 -4.979941338048793 -33.21480583100539
VL20-all 0.14687059748987089 0.10130958145121319
RT-VL20-all -18.261064964198983 1.3877003679459634 -16.620617406678317 -20.362940185406295
*** budget = 30
VL30-1 0.23227330396658566 0.06606324573492053
RT-VL30-1 -16.359716559774956 7.3675644873600925 -7.631675250761605 -43.23278096865015
VL30-2 0.07975590586013624 0.05550096581283392
RT-VL30-2 -15.670088014698422 6.365526752309105 -7.631675250761605 -30.8979230402217
VL30-3 0.0008045034532961282 0.0001777388810342991
RT-VL30-3 -20.544398209026834 1.3165411147851693 -20.000024138165212 -27.315851209400442
VL30-4 0.09454814786125701 0.04851285648802084
RT-VL30-4 -17.66173011237574 7.38176880331689 -4.979941338048793 -30.130979943517904
VL30-5 0.25141193963233627 0.05419139047187788
RT-VL30-5 -17.66173011237574 7.38176880331689 -4.979941338048793 -30.130979943517904
VL30-all 0.13175876015472227 0.09555940638897947
RT-VL30-all -17.579532601650335 1.6696029910130972 -15.670088014698422 -20.544398209026834
*** budget = 40
VL40-1 0.2200510197720751 0.0638747443985312
RT-VL40-1 -14.768085767053552 7.561563581984644 -7.4104632126924335 -43.23278096865015
VL40-2 0.0689426792433867 0.05227728760564988
RT-VL40-2 -13.752209639517199 5.973302669554247 -7.4104632126924335 -28.333181444981612
VL40-3 0.0007754569246537352 0.00019765535856179484
RT-VL40-3 -20.572779523196505 1.3938329353798555 -20.000024138165212 -27.315851209400442
VL40-4 0.08280716536991511 0.04835963678428836
RT-VL40-4 -17.064916079967254 8.384477271335626 -4.979941338048793 -33.21480583100539
VL40-5 0.2385690461416439 0.054978051028578744
RT-VL40-5 -17.064916079967254 8.384477271335626 -4.979941338048793 -33.21480583100539
VL40-all 0.12222907349033492 0.09192239122154569
RT-VL40-all -16.64458141794035 2.3524945586656036 -13.752209639517199 -20.572779523196505
*** budget = 50
VL50-1 0.20856068706968584 0.06450839929236513
RT-VL50-1 -13.654622049433604 7.347976796040192 -7.631675250761605 -43.23278096865015
VL50-2 0.061031690225603515 0.050503182080254116
RT-VL50-2 -12.505241140972712 4.9306022315841656 -7.631675250761605 -26.06185085088231
VL50-3 0.000746271885269271 0.00020900078390437824
RT-VL50-3 -20.816385108192282 1.5801851543275993 -20.000024138165212 -27.315851209400442
VL50-4 0.07419150571565636 0.04330245464125076
RT-VL50-4 -16.53277286398536 7.941660647626691 -4.979941338048793 -30.130979943517904
VL50-5 0.22896449200078842 0.05121895099850287
RT-VL50-5 -16.53277286398536 7.941660647626691 -4.979941338048793 -30.130979943517904
VL50-all 0.11469892937940067 0.088738808238709
RT-VL50-all -16.008358805313865 2.8802446849264762 -12.505241140972712 -20.816385108192282
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00011_11_clip_discriminator=10,dr_cc=0.7000,seed=3_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s3/04.09_10.37.14/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00011_11_clip_discriminator=10,dr_cc=0.7000,seed=3_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s3/04.09_10.37.14/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00011_11_clip_discriminator=10,dr_cc=0.7000,seed=3_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s3/04.09_10.37.14/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.67245748517971 2.3255670923271654 -5.543361765057175 -29.957231650535856
LR -0.19534757675093486 0.03253260183191889 -0.007287207653129402 -0.38938310792250563
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.28138976586281245 0.05259732734972976
RT-VL10-1 -17.962694022491995 5.291468312705672 -5.543361765057175 -28.10374461491974
VL10-2 0.12326426142139639 0.048040357584022045
RT-VL10-2 -17.962694022491995 5.291468312705672 -5.543361765057175 -28.10374461491974
VL10-3 0.0008742266172901969 0.00012347358164136685
RT-VL10-3 -20.01247905837068 0.26365729962144996 -18.462915643108055 -22.03020111138142
VL10-4 0.15465100881393795 0.018527224183264728
RT-VL10-4 -19.76710440949328 2.544369536379904 -7.734662903074863 -27.7524019372946
VL10-5 0.31360436407033326 0.02580488135322835
RT-VL10-5 -19.76710440949328 2.544369536379904 -7.734662903074863 -27.7524019372946
VL10-all 0.17475672535715403 0.11308413606834261
RT-VL10-all -19.094415184468247 0.9283801420133956 -17.962694022491995 -20.01247905837068
*** budget = 20
VL20-1 0.2528143564088502 0.057611838740570284
RT-VL20-1 -16.684437368812777 6.49564466620838 -5.543361765057175 -28.10374461491974
VL20-2 0.09652545632030572 0.051149972045743244
RT-VL20-2 -16.683618817184488 6.496794584330606 -5.543361765057175 -28.10374461491974
VL20-3 0.0008523536594472958 0.00016800387340314826
RT-VL20-3 -20.026207346966938 0.3722007830586142 -18.462915643108055 -22.03020111138142
VL20-4 0.1487588313470399 0.024737347998821373
RT-VL20-4 -19.77078137332689 3.305451703533975 -7.734662903074863 -27.7524019372946
VL20-5 0.3066655418598305 0.03507627575319515
RT-VL20-5 -19.77078137332689 3.305451703533975 -7.734662903074863 -27.7524019372946
VL20-all 0.16112330791909474 0.10918088818560566
RT-VL20-all -18.587165255923594 1.5567015657070329 -16.683618817184488 -20.026207346966938
*** budget = 30
VL30-1 0.23109108241413495 0.055390252604125875
RT-VL30-1 -14.688568934426588 6.673464129962517 -5.543361765057175 -25.89937027250631
VL30-2 0.07662829380193809 0.047366729074625656
RT-VL30-2 -14.647278496738679 6.717625416561481 -5.543361765057175 -25.89937027250631
VL30-3 0.0008283775374191738 0.0002015276381421899
RT-VL30-3 -20.039298951367808 0.45528668336203537 -18.462915643108055 -22.03020111138142
VL30-4 0.14314055805083964 0.028561406410992515
RT-VL30-4 -19.622694981972227 4.024174921265932 -7.734662903074863 -27.7524019372946
VL30-5 0.30000062382002546 0.041289207128297
RT-VL30-5 -19.622694981972227 4.024174921265932 -7.734662903074863 -27.7524019372946
VL30-all 0.15033778712487145 0.10654389936094093
RT-VL30-all -17.72410726929551 2.5000300561506514 -14.647278496738679 -20.039298951367808
*** budget = 40
VL40-1 0.21732484892570283 0.052105813765341576
RT-VL40-1 -14.571881782797396 6.814383041333366 -5.543361765057175 -25.89937027250631
VL40-2 0.06434880775903591 0.04297082795388109
RT-VL40-2 -14.652939875630443 6.747785371398743 -5.543361765057175 -25.89937027250631
VL40-3 0.0008012316338055664 0.00023153190644637493
RT-VL40-3 -19.99958873583356 0.41288873023967865 -18.462915643108055 -21.81450465009892
VL40-4 0.13766344250387383 0.03131514472879006
RT-VL40-4 -19.59570623988176 4.603290056571782 -7.734662903074863 -27.7524019372946
VL40-5 0.2934202881379087 0.04616154681766932
RT-VL40-5 -19.59570623988176 4.603290056571782 -7.734662903074863 -27.7524019372946
VL40-all 0.14271172379206537 0.10448445754958667
RT-VL40-all -17.683164574804984 2.5117242871954 -14.571881782797396 -19.99958873583356
*** budget = 50
VL50-1 0.21574298478409284 0.05482385726244089
RT-VL50-1 -14.295929433194022 7.064542899182698 -5.543361765057175 -25.89937027250631
VL50-2 0.06426207519862244 0.04421900685436608
RT-VL50-2 -14.327083684874932 7.05624050971348 -5.543361765057175 -25.89937027250631
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VL50-4 0.13207347900249194 0.03236171263215026
RT-VL50-4 -19.286915182636747 4.894748546935892 -7.734662903074863 -27.7524019372946
VL50-5 0.28684025528446827 0.04901258960388122
RT-VL50-5 -19.286915182636747 4.894748546935892 -7.734662903074863 -27.7524019372946
VL50-all 0.13994117957277774 0.10244836409707012
RT-VL50-all -17.438930615547623 2.566710084953216 -14.295929433194022 -19.997809594395665
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00007_7_clip_discriminator=10,dr_cc=0.7000,seed=2_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s2/04.09_10.37.13/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00007_7_clip_discriminator=10,dr_cc=0.7000,seed=2_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s2/04.09_10.37.13/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00007_7_clip_discriminator=10,dr_cc=0.7000,seed=2_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s2/04.09_10.37.13/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -19.985651935946105 0.32407551853902417 -11.425776934976017 -20.000024138165212
LR -0.1997522091262577 0.005554214016289127 -0.06660426125688645 -0.2000004781111453
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.31853300823612507 0.00438877048400126
RT-VL10-1 -19.856302115974152 1.015706158033393 -11.425776934976017 -20.000024138165212
VL10-2 0.15853300823612507 0.0043887704840012585
RT-VL10-2 -19.856302115974152 1.015706158033393 -11.425776934976017 -20.000024138165212
VL10-3 0.0009003059035035362 1.9929118443941018e-17
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.16090030590350357 2.0269806481627057e-17
RT-VL10-4 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-5 0.32090030590350355 5.551115123125783e-17
RT-VL10-5 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-all 0.19195338683655216 0.11935845488758516
RT-VL10-all -19.942535329288784 0.07040912383380982 -19.856302115974152 -20.00002413816521
*** budget = 20
VL20-1 0.3179663223757536 0.006154701210764939
RT-VL20-1 -19.7125800937831 1.4219725593725572 -11.425776934976017 -20.000024138165212
VL20-2 0.15796632237575362 0.00615470121076494
RT-VL20-2 -19.7125800937831 1.4219725593725572 -11.425776934976017 -20.000024138165212
VL20-3 0.0009003059035035325 2.769384296563588e-17
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.16090030590350357 2.866583523299505e-17
RT-VL20-4 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-5 0.32090030590350355 5.551115123125783e-17
RT-VL20-5 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-all 0.19172671249240358 0.11927028554831362
RT-VL20-all -19.885046520412363 0.14081824766761789 -19.7125800937831 -20.00002413816521
*** budget = 30
VL30-1 0.31739963651538217 0.007473762231811521
RT-VL30-1 -19.56885807159205 1.7236707575476096 -11.425776934976017 -20.000024138165212
VL30-2 0.1573996365153821 0.007473762231811519
RT-VL30-2 -19.56885807159205 1.7236707575476096 -11.425776934976017 -20.000024138165212
VL30-3 0.000900305903503531 3.011959688036681e-17
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.16090030590350357 3.14018491736755e-17
RT-VL30-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-5 0.3209003059035036 3.14018491736755e-17
RT-VL30-5 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-all 0.191500038148255 0.11918269765438154
RT-VL30-all -19.82755771153595 0.2112273715014277 -19.56885807159205 -20.000024138165216
*** budget = 40
VL40-1 0.3168023189868826 0.008608735954626459
RT-VL40-1 -19.417367291444723 1.981578403297287 -11.425776934976017 -20.000024138165212
VL40-2 0.15680231898688243 0.00860873595462646
RT-VL40-2 -19.417367291444723 1.981578403297287 -11.425776934976017 -20.000024138165212
VL40-3 0.0009003059035035279 3.447431623948558e-17
RT-VL40-3 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-4 0.16090030590350352 5.551115123125783e-17
RT-VL40-4 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-5 0.32090030590350355 5.551115123125783e-17
RT-VL40-5 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-all 0.19126111113685512 0.11909100613239554
RT-VL40-all -19.76696139947702 0.2854423939208493 -19.417367291444723 -20.00002413816522
*** budget = 50
VL50-1 0.3162662647946393 0.009480710654761302
RT-VL50-1 -19.281414027209937 2.178342168733152 -11.425776934976017 -20.000024138165212
VL50-2 0.15626626479463915 0.009480710654761299
RT-VL50-2 -19.281414027209937 2.178342168733152 -11.425776934976017 -20.000024138165212
VL50-3 0.000900305903503525 3.7740359695528306e-17
RT-VL50-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-4 0.16090030590350352 5.551115123125783e-17
RT-VL50-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-5 0.32090030590350355 5.551115123125783e-17
RT-VL50-5 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-all 0.19104668945995784 0.11900927132916701
RT-VL50-all -19.712580093783103 0.35204561916904736 -19.281414027209937 -20.000024138165216
logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00003_3_clip_discriminator=10,dr_cc=0.7000,seed=1_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s1/04.09_10.37.16/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00003_3_clip_discriminator=10,dr_cc=0.7000,seed=1_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s1/04.09_10.37.16/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/abl/run_2024-04-09_10-36-43/run_95b09_00003_3_clip_discriminator=10,dr_cc=0.7000,seed=1_2024-04-09_10-36-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg00_ds0.001_zt=p_g1_s1/04.09_10.37.16/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -18.19975283261665 4.3668159549497325 -3.7619363991413697 -26.49076422916725
LR -0.17254001968407773 0.0650577875567896 -0.00014294521744084854 -0.3014534104138873
VL-std-all 0.0 0.0
*** budget = 10
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RT-VL10-1 -18.223932315421248 3.4965519428549174 -6.654997063715167 -22.49420054451211
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VL10-4 0.06769762146858997 0.05190483055415074
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VL10-5 0.2219245306721256 0.05916655341736849
RT-VL10-5 -11.246331257728604 6.27623445417112 -3.7619363991413697 -26.49076422916725
VL10-all 0.14850391657187706 0.10819028217364923
RT-VL10-all -15.767615625402735 3.762002233791605 -11.202860477151098 -19.94102176129148
*** budget = 20
VL20-1 0.2956699924067246 0.028366087691769216
RT-VL20-1 -17.423995167322666 3.803918625635032 -6.654997063715167 -22.49420054451211
VL20-2 0.1356699924067246 0.028366087691769213
RT-VL20-2 -17.423995167322666 3.803918625635032 -6.654997063715167 -22.49420054451211
VL20-3 0.0008672961329046381 0.00014990909015540207
RT-VL20-3 -19.881965147524518 1.0131799397725894 -11.166276283266011 -20.0002630278893
VL20-4 0.03991947128290588 0.03599267184603764
RT-VL20-4 -9.800025413026328 6.015418759782699 -3.7619363991413697 -26.49076422916725
VL20-5 0.18924460281668873 0.04686640450001645
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VL20-all 0.1322742710091897 0.10559594071715604
RT-VL20-all -14.94476223899245 4.1402617232601235 -9.800025413026328 -19.881965147524518
*** budget = 30
VL30-1 0.29007062873876455 0.029962367158200325
RT-VL30-1 -17.02325673414012 3.6518694817722404 -6.654997063715167 -21.262290861314906
VL30-2 0.13007062873876454 0.02996236715820033
RT-VL30-2 -17.02325673414012 3.6518694817722404 -6.654997063715167 -21.262290861314906
VL30-3 0.000850791247605187 0.00018136115695094852
RT-VL30-3 -19.82293565220418 1.2366676762910311 -11.166276283266011 -20.0002630278893
VL30-4 0.025345379573874345 0.022451564065061887
RT-VL30-4 -9.042301366152133 5.781382987502629 -3.7619363991413697 -25.725662889928337
VL30-5 0.1699460348815496 0.03625646759869862
RT-VL30-5 -9.616246526468348 5.9808698122627675 -3.7619363991413697 -25.725662889928337
VL30-all 0.12325669263611164 0.10452085304176086
RT-VL30-all -14.505599402620978 4.35211807403848 -9.042301366152133 -19.82293565220418
*** budget = 40
VL40-1 0.2837591173054602 0.031987518681158504
RT-VL40-1 -16.944977379279603 3.398990985713678 -8.263036938344813 -20.866807796884864
VL40-2 0.12375911730546012 0.0319875186811585
RT-VL40-2 -16.944977379279603 3.398990985713678 -8.263036938344813 -20.866807796884864
VL40-3 0.0008333942063436037 0.0002080490480725114
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VL40-4 0.01958677487757921 0.018759784813114768
RT-VL40-4 -9.201949604681822 6.25261569619534 -3.7619363991413697 -25.725662889928337
VL40-5 0.16045212059350736 0.033195857672914476
RT-VL40-5 -8.697354688269984 5.525351725689143 -3.7619363991413697 -25.725662889928337
VL40-all 0.1176781048576701 0.10268796741069339
RT-VL40-all -14.309994884972804 4.498677047670827 -8.697354688269984 -19.76071537335301
*** budget = 50
VL50-1 0.27974104554945606 0.03220441993443714
RT-VL50-1 -17.139725096694736 3.2773595351014198 -6.654997063715167 -20.866807796884864
VL50-2 0.11974104554945608 0.03220441993443714
RT-VL50-2 -17.139725096694736 3.2773595351014198 -6.654997063715167 -20.866807796884864
VL50-3 0.0008177814770062852 0.00022824475831184092
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VL50-4 0.016684552688413363 0.018697951252692215
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VL50-5 0.1554721796810112 0.03364405773992803
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VL50-all 0.1144913209890686 0.10149118125728093
RT-VL50-all -14.200616651824955 4.743372696799038 -8.24110840887374 -19.70487666156349
logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00033_33_dl_scale=0.5000,dr_cc=0.8000,seed=5_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s5/04.08_23.20.28/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00033_33_dl_scale=0.5000,dr_cc=0.8000,seed=5_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s5/04.08_23.20.28/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00033_33_dl_scale=0.5000,dr_cc=0.8000,seed=5_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s5/04.08_23.20.28/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -10.333810856438918 5.812157393932942 -4.2976421959904085 -27.432067676593313
LR -0.06714877224775195 0.07962149681003539 -6.834289085566603e-05 -0.2885110408899203
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.06253233468696207 0.061412537914514245
RT-VL10-1 -7.724262647461922 2.050900673331052 -5.404226371010674 -21.29491032780071
VL10-2 0.03868651793364021 0.03119970262650641
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VL10-3 0.0027686326893416784 0.0073105641234226856
RT-VL10-3 -18.370449563896614 4.278247065725787 -6.252852654962218 -20.000024138165212
VL10-4 0.037072304341432556 0.036179573213093126
RT-VL10-4 -7.056488908944209 2.334824959924026 -5.389065295224089 -20.000024138165212
VL10-5 0.080207535852363 0.06250120521731407
RT-VL10-5 -7.070594895507654 1.8415838155587225 -5.391495176464799 -20.000024138165212
VL10-all 0.0442534651007479 0.026192989778858495
RT-VL10-all -9.647081430695838 4.377489265285828 -7.056488908944209 -18.370449563896614
*** budget = 20
VL20-1 0.0324301750700693 0.03424441732647852
RT-VL20-1 -7.793615240852475 1.133403385686815 -5.404226371010674 -14.585669604351484
VL20-2 0.02053490132298888 0.016474000977961874
RT-VL20-2 -7.860229025398948 4.232262456993542 -5.965850773431957 -23.625752046844127
VL20-3 0.0009255460708574385 0.0004835005048590611
RT-VL20-3 -18.52728137350721 4.0915276834873495 -6.252852654962218 -20.000024138165212
VL20-4 0.020583883781973462 0.021181765625027334
RT-VL20-4 -6.579860461875612 0.6838214144775588 -5.5633859463847415 -10.936055468037605
VL20-5 0.047271716541784234 0.04165303838797864
RT-VL20-5 -6.996010922365631 0.7856013990181538 -5.391495176464799 -8.624478471206837
VL20-all 0.024349244557534663 0.01528488120132404
RT-VL20-all -9.551399404799975 4.513897950127491 -6.579860461875612 -18.52728137350721
*** budget = 30
VL30-1 0.018932476857011823 0.023094096642735602
RT-VL30-1 -8.000690534976556 0.6724246241457832 -5.7754627101078295 -8.91756989745813
VL30-2 0.014927658639547349 0.013795738157372058
RT-VL30-2 -7.442993491186225 3.7103830391132933 -5.965850773431957 -23.215349147376426
VL30-3 0.0008335318813781667 0.00018749315426214722
RT-VL30-3 -18.31645452246036 4.315603157352072 -6.252852654962218 -20.000024138165212
VL30-4 0.012651007986442798 0.014394850894179155
RT-VL30-4 -6.632482769216879 0.7844565367589216 -5.426906884668528 -10.936055468037605
VL30-5 0.029830361616484467 0.02442873936562837
RT-VL30-5 -7.259148914139086 0.6471608481364014 -5.416150786834834 -8.565700066840172
VL30-all 0.015435007396172918 0.009387967388536457
RT-VL30-all -9.530354046395823 4.4147148676443875 -6.632482769216879 -18.31645452246036
*** budget = 40
VL40-1 0.015533738737924132 0.017345499566984886
RT-VL40-1 -7.984571407488703 0.6955263800070788 -5.7754627101078295 -8.896186128045485
VL40-2 0.009485064990290708 0.007840221534253629
RT-VL40-2 -7.196357656294278 3.4043643020940424 -5.965850773431957 -23.215349147376426
VL40-3 0.0008100707384692518 0.0002130444948434129
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VL40-4 0.010537580463783964 0.010660143294911184
RT-VL40-4 -6.751314131931161 0.9071789774018012 -5.5633859463847415 -10.936055468037605
VL40-5 0.021484694172231953 0.02059757108792404
RT-VL40-5 -7.249786792167642 0.47235531772239514 -5.90549902753621 -8.100008643542862
VL40-all 0.011570229820540002 0.006858590334083833
RT-VL40-all -9.381392010154034 4.19049306435097 -6.751314131931161 -17.724930062888383
*** budget = 50
VL50-1 0.010698164742303446 0.00997759626004757
RT-VL50-1 -8.187425663106925 0.3389336244064123 -7.414937018804041 -8.896186128045485
VL50-2 0.008253002028991248 0.006791040698705196
RT-VL50-2 -6.429111548807465 0.4411905282127673 -6.003893468639732 -8.14519795033182
VL50-3 0.0007911985445991735 0.0002323217953954017
RT-VL50-3 -17.635623295253335 4.932077113134743 -6.252852654962218 -20.000024138165212
VL50-4 0.007774635144298414 0.007504781389735888
RT-VL50-4 -6.868840743276139 0.9142177668144867 -5.729993956495399 -10.936055468037605
VL50-5 0.018181124270635066 0.01828433679933972
RT-VL50-5 -7.213375092778321 0.4303899575290701 -6.334701157172713 -7.937898699914211
VL50-all 0.00913962494616547 0.005595056336970322
RT-VL50-all -9.266875268644437 4.224251187382893 -6.429111548807465 -17.635623295253335
logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00025_25_dl_scale=0.5000,dr_cc=0.8000,seed=4_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s4/04.08_23.20.23/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00025_25_dl_scale=0.5000,dr_cc=0.8000,seed=4_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s4/04.08_23.20.23/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00025_25_dl_scale=0.5000,dr_cc=0.8000,seed=4_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s4/04.08_23.20.23/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -10.771230284235791 7.991799432248234 -4.243232256805701 -47.30174463147686
LR -0.10233350166151123 0.1712932278178927 -6.704879376662198e-05 -0.8625167587119714
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.08827465715450049 0.06901911576676048
RT-VL10-1 -7.191750988179811 3.0307369398329116 -4.940656741102337 -26.4145392674915
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VL10-4 0.04244548664544219 0.03654213988324462
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VL10-5 0.07211873989102374 0.061400500228271565
RT-VL10-5 -10.055747614822119 11.18227330827899 -4.5373715623508035 -46.31111747341412
VL10-all 0.048700401663613516 0.028665239264615283
RT-VL10-all -9.862439295956198 4.115843940322894 -6.786363779599711 -17.7698706044
*** budget = 20
VL20-1 0.0494776689055074 0.04499602998390785
RT-VL20-1 -7.3986512520807945 2.64028134119712 -5.002015785311655 -19.30312053329056
VL20-2 0.017466835805175645 0.015417733501267903
RT-VL20-2 -6.988694472697854 3.678458401108495 -5.046501109137098 -24.68431201860291
VL20-3 0.000980923758652891 0.0007484884959616678
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VL20-4 0.02070229127435054 0.016921065532818007
RT-VL20-4 -6.566022324911116 3.7757727074794434 -4.836914328267379 -29.29888720099439
VL20-5 0.04156375112616559 0.034380682190329366
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VL20-all 0.02603829417397041 0.017437079681087906
RT-VL20-all -10.04266023707121 4.928013833785034 -6.566022324911116 -19.676516944597594
*** budget = 30
VL30-1 0.03434115763797364 0.03319310102218596
RT-VL30-1 -7.694725599259433 2.649479838230436 -5.002015785311655 -19.30312053329056
VL30-2 0.012146281222176099 0.011413879266873208
RT-VL30-2 -6.926103611837022 3.776384464178837 -5.028865540722146 -24.68431201860291
VL30-3 0.000891161879223672 6.400816995907554e-05
RT-VL30-3 -19.738395966530806 1.8313972014408597 -6.918615556444786 -20.000024138165212
VL30-4 0.01621367898628651 0.014269904102248246
RT-VL30-4 -6.398409261800925 3.3323919286080623 -4.836914328267379 -29.29888720099439
VL30-5 0.02546792728313164 0.02157768951631948
RT-VL30-5 -7.3138144799625096 8.334452839998402 -4.665156040088964 -42.292981405862
VL30-all 0.017812041401758315 0.011426250800842709
RT-VL30-all -9.614289783878139 5.080210068653682 -6.398409261800925 -19.738395966530806
*** budget = 40
VL40-1 0.02333211171972389 0.02085623464122999
RT-VL40-1 -7.155502405237216 1.0868269423515318 -5.002015785311655 -10.524591803667466
VL40-2 0.007901761504246803 0.0068146483796311325
RT-VL40-2 -7.410222502584024 4.660040994603061 -4.940656741102337 -24.68431201860291
VL40-3 0.0008879491139361508 7.414073740433461e-05
RT-VL40-3 -19.646472554875476 2.1213094997384476 -6.918615556444786 -20.000024138165212
VL40-4 0.011426786882146324 0.008240667831647758
RT-VL40-4 -5.914099334375789 0.6464494296662745 -5.114219197867418 -8.11839155336459
VL40-5 0.019377586846797454 0.01584644994793584
RT-VL40-5 -5.968356126789869 5.042923771110346 -4.5373715623508035 -36.177378254365216
VL40-all 0.012585239213370125 0.008021656013156351
RT-VL40-all -9.218930584772476 5.248827225531834 -5.914099334375789 -19.646472554875476
*** budget = 50
VL50-1 0.016832637277273896 0.015258381167794818
RT-VL50-1 -7.957059192227418 2.554181525424285 -5.002015785311655 -19.30312053329056
VL50-2 0.006186251809233434 0.006318753954748548
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VL50-3 0.0008850658630370934 8.207012957921372e-05
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VL50-4 0.009879084553219212 0.007637618772221186
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VL50-5 0.015162673403489616 0.01503551352181776
RT-VL50-5 -6.1476728217021614 5.583079890286406 -4.7108484522640195 -36.177378254365216
VL50-all 0.00978914258125065 0.005843922393524761
RT-VL50-all -9.340346864119068 5.161658662575504 -5.952486352891019 -19.563977185441203
logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00017_17_dl_scale=0.5000,dr_cc=0.8000,seed=3_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s3/04.08_23.20.24/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00017_17_dl_scale=0.5000,dr_cc=0.8000,seed=3_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s3/04.08_23.20.24/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00017_17_dl_scale=0.5000,dr_cc=0.8000,seed=3_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s3/04.08_23.20.24/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.672669439547319 4.8424602220432496 -4.250184587513585 -38.38358173901465
LR -0.05030944913927882 0.06491996857327152 -3.899595945777712e-05 -0.5624828157144662
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.11265387306386583 0.05732827229621504
RT-VL10-1 -5.991849114847125 2.3153594959634596 -4.392062646602415 -20.000024138165212
VL10-2 0.030985453457699142 0.034599633343564994
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VL10-3 0.022064584460417147 0.02863271290105269
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VL10-4 0.027059901874819065 0.02960713098293431
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VL10-5 0.051406512956222235 0.05045999052617198
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VL10-all 0.04883406516260468 0.03343314999202198
RT-VL10-all -7.66399307458977 3.097344825938352 -5.438039971337447 -13.801031679581387
*** budget = 20
VL20-1 0.08536532782699931 0.023584469044505244
RT-VL20-1 -5.969073756385339 1.5170617030539206 -4.49052928327319 -10.516962157037323
VL20-2 0.014449649206725947 0.012016820566205531
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VL20-3 0.0070763323273750345 0.010666926267252469
RT-VL20-3 -16.9439887380947 5.835098520901309 -5.013999226953399 -23.5297632048721
VL20-4 0.013291234288866 0.013436539732760206
RT-VL20-4 -6.457292944286992 2.28962604716841 -4.691131267458914 -25.24219939640576
VL20-5 0.025392316395854472 0.025097414558347384
RT-VL20-5 -6.052658888757303 2.1426808334412173 -4.742777091585304 -23.753355095183927
VL20-all 0.02911497200916415 0.028737170013112278
RT-VL20-all -8.111105558082508 4.437396426572308 -5.132513462888212 -16.9439887380947
*** budget = 30
VL30-1 0.07767302742799308 0.020836426803882046
RT-VL30-1 -6.0569332293827935 1.5048892619008936 -4.49052928327319 -9.652803912300588
VL30-2 0.010056745506788447 0.008120410808706046
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VL30-3 0.004973852092148711 0.008472541323527358
RT-VL30-3 -18.18279640712238 4.866553865399855 -5.013999226953399 -23.5297632048721
VL30-4 0.00871166576957582 0.008717032175386776
RT-VL30-4 -6.159456331768914 0.6177711300888835 -4.792473190622172 -7.5107145254397905
VL30-5 0.014488877586677775 0.014140604934022975
RT-VL30-5 -6.296068407056848 4.330565507284117 -4.742777091585304 -36.35130995366262
VL30-all 0.023180833676636765 0.02741547726790724
RT-VL30-all -8.33803454736763 4.943986526883196 -4.994918361507219 -18.18279640712238
*** budget = 40
VL40-1 0.07399829193035033 0.018365410135969894
RT-VL40-1 -6.511908115647568 1.7784758741150062 -4.49052928327319 -10.516962157037323
VL40-2 0.0072897255381751385 0.0049759410182100795
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VL40-3 0.0019909003686368536 0.003520006066344368
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VL40-4 0.0059512702932872814 0.006087909418366707
RT-VL40-4 -6.261121944204646 0.61573441273051 -5.060702835719143 -7.5107145254397905
VL40-5 0.014630414478038516 0.01795720488045799
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VL40-all 0.020772120521697624 0.026925499744191998
RT-VL40-all -8.721183676122063 5.355008624346162 -4.936281949322179 -19.3664503026361
*** budget = 50
VL50-1 0.0679414579207323 0.014495042905889374
RT-VL50-1 -6.252072064663405 1.5249924264396133 -4.49052928327319 -9.652803912300588
VL50-2 0.005854329143982495 0.004461578468199963
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VL50-3 0.001197342074745892 0.0018788017524632857
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VL50-4 0.004958453584232403 0.004237582676621731
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VL50-5 0.010428092200751271 0.012304611282755944
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VL50-all 0.018075934984888874 0.025105309172242383
RT-VL50-all -8.496808737626385 5.428779150179813 -4.824812123140867 -19.30079479036749
logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00009_9_dl_scale=0.5000,dr_cc=0.8000,seed=2_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s2/04.08_23.20.24/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00009_9_dl_scale=0.5000,dr_cc=0.8000,seed=2_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s2/04.08_23.20.24/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00009_9_dl_scale=0.5000,dr_cc=0.8000,seed=2_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s2/04.08_23.20.24/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -8.493029839269056 6.477496802522636 -4.012327502413214 -40.01684590747097
LR -0.0656243631358206 0.09317049175083497 -0.00012194104462670552 -0.4318225312576488
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.040181255244886645 0.04022315322335359
RT-VL10-1 -7.40050897358513 2.6720909411890212 -4.174061014946813 -23.532444384634793
VL10-2 0.0352006551868085 0.030345792352454364
RT-VL10-2 -6.146007800521169 4.543700507874277 -4.034088549712542 -40.01684590747097
VL10-3 0.03797754534856038 0.037572920258832174
RT-VL10-3 -7.41783953803874 5.602701361084319 -4.012327502413214 -29.666797598008607
VL10-4 0.03895214354020486 0.037186500645722834
RT-VL10-4 -6.1270139806250015 3.2141841279210595 -4.048438356269401 -26.45757464285357
VL10-5 0.07197457239813353 0.05657864412905199
RT-VL10-5 -6.411780641787928 3.709757861706329 -4.102165957606701 -27.822691742862254
VL10-all 0.044857234343718785 0.013657705098380529
RT-VL10-all -6.700630186911593 0.5872469961946205 -6.1270139806250015 -7.41783953803874
*** budget = 20
VL20-1 0.020149197305228055 0.024400515206423546
RT-VL20-1 -7.261223337535106 2.323970709051148 -4.694687456291039 -23.532444384634793
VL20-2 0.0189026716180466 0.015393249650206081
RT-VL20-2 -5.189117998711269 0.8381180222488234 -4.034088549712542 -7.402722778999027
VL20-3 0.01791677378129589 0.02002463262863171
RT-VL20-3 -7.0080438163219165 4.4415077233699245 -4.048438356269401 -23.746882971069397
VL20-4 0.017117368367616126 0.01697935818529717
RT-VL20-4 -5.994859766488 3.2385768618081876 -4.068193496316204 -26.45757464285357
VL20-5 0.04001776203728164 0.03239922739247438
RT-VL20-5 -5.686496260626062 1.1053107842738548 -4.205539281164972 -10.100138624005421
VL20-all 0.022820754621893664 0.008657982441846436
RT-VL20-all -6.22794823593647 0.7877658647150585 -5.189117998711269 -7.261223337535106
*** budget = 30
VL30-1 0.013973855480919482 0.015172446669619059
RT-VL30-1 -7.525653468642406 2.706048391336837 -5.3938566181667955 -23.532444384634793
VL30-2 0.015245527133351242 0.012666033808906109
RT-VL30-2 -5.189169224951634 0.9122856539858865 -4.034088549712542 -7.402722778999027
VL30-3 0.010541756351910469 0.011550555510526255
RT-VL30-3 -7.689299033782894 5.499235406862051 -4.107362847977299 -24.94306862644662
VL30-4 0.012101860481305746 0.012007638119700901
RT-VL30-4 -6.134654704993088 3.7020770476717946 -4.068193496316204 -26.45757464285357
VL30-5 0.02693717976882424 0.020639651899006555
RT-VL30-5 -5.7847403172967855 1.0053163235698135 -4.3925284805957965 -10.100138624005421
VL30-all 0.015760035843262236 0.0058137147102486665
RT-VL30-all -6.4647033499333615 0.9821933497090866 -5.189169224951634 -7.689299033782894
*** budget = 40
VL40-1 0.010959059453606262 0.011516604977876507
RT-VL40-1 -7.0602174501079045 1.0400557605112861 -5.578108501293491 -10.229435930451489
VL40-2 0.011149275296830071 0.010219982489531015
RT-VL40-2 -5.177786509023788 0.8501584396048664 -4.034088549712542 -7.053712114656601
VL40-3 0.008233583415434544 0.010105116940663747
RT-VL40-3 -8.061646043707174 5.61196681712089 -4.107362847977299 -23.746882971069397
VL40-4 0.007743407996975218 0.008329542971455944
RT-VL40-4 -5.382649247031966 1.9533107448666733 -4.068193496316204 -12.391196044916011
VL40-5 0.021329468111425356 0.015821200375559195
RT-VL40-5 -5.910645796454683 1.073076933810052 -4.558032613739128 -10.100138624005421
VL40-all 0.011882958854854289 0.004921021056736738
RT-VL40-all -6.318589009265104 1.0892283339638993 -5.177786509023788 -8.061646043707174
*** budget = 50
VL50-1 0.0071926362310174114 0.007015884094572407
RT-VL50-1 -6.8674387378281505 0.9101124110019736 -5.578108501293491 -9.219726186919898
VL50-2 0.010980590369202507 0.009988819211231603
RT-VL50-2 -5.11301303010676 0.8501737607743645 -4.034088549712542 -6.996097260900194
VL50-3 0.00567521226730402 0.006054930429887407
RT-VL50-3 -8.35969732603009 5.987595904385258 -4.107362847977299 -23.746882971069397
VL50-4 0.005690918712521039 0.00542164461943444
RT-VL50-4 -5.574170769842889 2.2542503313562303 -4.068193496316204 -12.391196044916011
VL50-5 0.01869121076811137 0.014828488781941793
RT-VL50-5 -5.89341239695348 0.8103548533492171 -4.4076057979420575 -7.426419194518188
VL50-all 0.00964611366963127 0.004919873702041011
RT-VL50-all -6.3615464521522735 1.1529509602909191 -5.11301303010676 -8.35969732603009
logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00001_1_dl_scale=0.5000,dr_cc=0.8000,seed=1_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s1/04.08_23.20.28/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00001_1_dl_scale=0.5000,dr_cc=0.8000,seed=1_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s1/04.08_23.20.28/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/sn/run_2024-04-08_23-19-45/run_03730_00001_1_dl_scale=0.5000,dr_cc=0.8000,seed=1_2024-04-08_23-19-45/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg10_ds0.500_zt=p_g1_s1/04.08_23.20.28/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -13.663629629511039 6.403708323411468 -4.3676362100934325 -24.276980488538353
LR -0.11606489592433517 0.08507320528187767 -3.6697534405272236e-05 -0.26857547768605894
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.10709935750830372 0.08755804609763328
RT-VL10-1 -8.1614070396504 3.80317257179617 -4.3676362100934325 -22.719880322923565
VL10-2 0.05102683179631268 0.04380114616408968
RT-VL10-2 -7.9767195730059575 3.412712849486724 -4.596730879106605 -20.000024138165212
VL10-3 0.0008847811364158442 0.00010325809717037867
RT-VL10-3 -19.816341133138987 1.539143084267892 -6.572796731886898 -20.000024138165212
VL10-4 0.05430019471489488 0.045400665513043816
RT-VL10-4 -9.212216961458001 4.608297650544244 -5.135497766416302 -24.276980488538353
VL10-5 0.09735604783094225 0.08151867499155128
RT-VL10-5 -8.862168893550404 4.649529752189969 -4.993266967199949 -23.65852937830202
VL10-all 0.06213344259737388 0.037943854653554744
RT-VL10-all -10.805770720160748 4.52777142703864 -7.9767195730059575 -19.816341133138987
*** budget = 20
VL20-1 0.0635830553064474 0.05011422738715888
RT-VL20-1 -7.3399237218633075 2.5075286175276883 -4.3676362100934325 -22.719880322923565
VL20-2 0.024055825998332628 0.021038360026716295
RT-VL20-2 -6.824580689156401 0.6457550220329449 -5.275782881278715 -8.643343706483506
VL20-3 0.0008692563693281486 0.00014436908421209968
RT-VL20-3 -19.632658128112766 2.161120999626915 -6.572796731886898 -20.000024138165212
VL20-4 0.030414341963172033 0.028654258966094318
RT-VL20-4 -7.803521848155038 2.460884481596276 -6.235646775484677 -19.26260716242025
VL20-5 0.05114686756484686 0.044170245703671854
RT-VL20-5 -8.212993536744973 4.244090425002611 -5.135497766416302 -23.65852937830202
VL20-all 0.034013869440425414 0.021810827496441504
RT-VL20-all -9.962735584806497 4.857125928084011 -6.824580689156401 -19.632658128112766
*** budget = 30
VL30-1 0.04273229567963471 0.034255918150931326
RT-VL30-1 -7.274876114638201 2.5791564036543995 -4.4959966796570345 -22.719880322923565
VL30-2 0.01814837157706432 0.0185727033228174
RT-VL30-2 -6.834404742876035 0.6147469853324191 -5.461877208416494 -7.971103184059854
VL30-3 0.0008537316022404529 0.00017475867227402162
RT-VL30-3 -19.44897512308655 2.627631561603769 -6.572796731886898 -20.000024138165212
VL30-4 0.017193614212613437 0.018743704848279655
RT-VL30-4 -7.322732500997569 0.7422726960787586 -6.541267671785075 -10.479426070455554
VL30-5 0.033788788379305906 0.026816859790327906
RT-VL30-5 -8.632250568270429 5.117935842832769 -4.993266967199949 -23.65852937830202
VL30-all 0.022543360290171767 0.014508125079215593
RT-VL30-all -9.902647809973757 4.810853082851324 -6.834404742876035 -19.44897512308655
*** budget = 40
VL40-1 0.03345439963986387 0.02190850461690242
RT-VL40-1 -7.360221898956617 3.0034037736329493 -4.4959966796570345 -22.719880322923565
VL40-2 0.01448727148231901 0.014381768381813074
RT-VL40-2 -6.851024207762643 0.5306611612712728 -5.8505598295462935 -7.971103184059854
VL40-3 0.0008373676585534223 0.00020060198017078946
RT-VL40-3 -19.255363306977834 3.030867778457044 -6.572796731886898 -20.000024138165212
VL40-4 0.016356626193131992 0.024861014581581728
RT-VL40-4 -7.3197865998540435 0.7393226732545268 -6.541267671785075 -10.479426070455554
VL40-5 0.02940363514211589 0.025829122507590428
RT-VL40-5 -8.456420612961125 4.880313585337349 -4.993266967199949 -23.65852937830202
VL40-all 0.01890786002319684 0.011613407782130337
RT-VL40-all -9.848563325302452 4.732816133533877 -6.851024207762643 -19.255363306977834
*** budget = 50
VL50-1 0.030921120126874216 0.02266544610893452
RT-VL50-1 -7.3845180434402415 3.0808532810777804 -4.799439136983544 -22.719880322923565
VL50-2 0.010040604319314469 0.009304548779508516
RT-VL50-2 -6.71610781137615 0.5000901417859575 -5.8505598295462935 -7.7378364847212655
VL50-3 0.0008226820680650615 0.00022020627896064373
RT-VL50-3 -19.08160911303411 3.3421577225707253 -6.572796731886898 -20.000024138165212
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logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00036_36_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=5_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s5/04.09_10.40.38/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00036_36_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=5_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s5/04.09_10.40.38/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00036_36_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=5_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s5/04.09_10.40.38/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
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LR -0.037526813508964425 0.06032792155355699 -4.550677330428243e-05 -0.3087153701684684
VL-std-all 0.0 0.0
*** budget = 10
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RT-VL10-all -7.234278098296606 3.1269001678395347 -4.980970834897419 -13.42405648343649
*** budget = 20
VL20-1 0.03951324333950505 0.02870260261418858
RT-VL20-1 -5.59745357514151 1.340996306079587 -4.091019330915991 -14.697479933938375
VL20-2 0.014073157579025566 0.012615761870865065
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VL20-3 0.004051089213070624 0.009903115079679014
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VL20-4 0.023750699158988333 0.0214779563768543
RT-VL20-4 -5.29371496918546 2.5582008965670155 -4.127582055927813 -22.35422273381192
VL20-5 0.03300657657938114 0.039249667286983494
RT-VL20-5 -6.2947615320942125 2.8733424353282557 -4.351416180103527 -19.88006161461773
VL20-all 0.022878953173994142 0.012744087284741263
RT-VL20-all -7.727160513223159 4.451945797852173 -4.867602697840209 -16.5822697918544
*** budget = 30
VL30-1 0.030050560786517088 0.02332690644159242
RT-VL30-1 -5.7744662418184545 1.5309088840703502 -4.091019330915991 -14.697479933938375
VL30-2 0.009995083870228606 0.008402359718634213
RT-VL30-2 -4.86077635422223 0.4938381451855008 -4.022013725721363 -5.3736814021458645
VL30-3 0.0019870413529390007 0.00347379955433264
RT-VL30-3 -16.62062128856679 6.213977151735943 -4.171127396466838 -20.000024138165212
VL30-4 0.014566944650124924 0.010885182272012418
RT-VL30-4 -5.302389666090736 2.612578735937592 -4.211070386334169 -22.35422273381192
VL30-5 0.019937511156048218 0.017202959220956885
RT-VL30-5 -5.940748899412748 1.724817321201931 -4.351416180103527 -12.222893085242466
VL30-all 0.015307428363171565 0.009433977428047574
RT-VL30-all -7.6998004900221915 4.476379226290735 -4.86077635422223 -16.62062128856679
*** budget = 40
VL40-1 0.02325080616641305 0.01530390132894387
RT-VL40-1 -5.814841929565121 1.6638391880909398 -4.296642535307905 -14.697479933938375
VL40-2 0.007349963132433139 0.0069418947061617945
RT-VL40-2 -4.8263671347088914 0.49696873429994814 -4.022013725721363 -5.3736814021458645
VL40-3 0.000770846885119741 0.0002747374676032654
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VL40-4 0.011441385379186975 0.008661983106794958
RT-VL40-4 -5.289589129422956 2.88174777819311 -4.211070386334169 -22.35422273381192
VL40-5 0.014352796778558434 0.013543412285623127
RT-VL40-5 -6.004682572614837 1.6213388652702834 -4.351416180103527 -11.589280955076893
VL40-all 0.011433159668342267 0.007463759442923729
RT-VL40-all -7.8992421660370535 4.848325045385077 -4.8263671347088914 -17.560730063873464
*** budget = 50
VL50-1 0.02088284815479944 0.013620239941753782
RT-VL50-1 -5.943675407526956 1.814757452977806 -4.296642535307905 -14.697479933938375
VL50-2 0.005923625085920514 0.005863001539005734
RT-VL50-2 -4.837012077360248 0.520525455281057 -4.022013725721363 -5.3736814021458645
VL50-3 0.000863189140779097 0.000511042997047197
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VL50-4 0.010327242246972571 0.008825019135853367
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VL50-all 0.009947931177167196 0.006658306732186914
RT-VL50-all -7.72338383765274 4.4210415364268485 -4.837012077360248 -16.533355732329362
logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00028_28_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=4_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s4/04.09_10.40.36/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00028_28_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=4_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s4/04.09_10.40.36/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00028_28_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=4_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s4/04.09_10.40.36/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -12.257932220304046 7.168816900300272 -4.833137313297071 -56.71012532549905
LR -0.1037012953770009 0.09226457932282525 -8.332883421502402e-06 -0.6299230864738274
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.07108901153842836 0.07628810737993713
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VL10-4 0.04503180946851081 0.0437568232127474
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VL10-5 0.10920772913245867 0.07794348763919122
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*** budget = 20
VL20-1 0.031210013215449556 0.033238595765456874
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VL20-4 0.022234595637305926 0.019533511692110758
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VL20-5 0.0642382198267025 0.04327950395051449
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VL20-all 0.028664824681232083 0.020497899322268927
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*** budget = 30
VL30-1 0.02042142704531697 0.018930139956625795
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VL30-3 0.0008606450728854487 0.00015638320942196394
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VL30-4 0.017816047736529707 0.015942451340682798
RT-VL30-4 -10.527571293246478 7.687189564582545 -5.661074005849526 -34.88316313301204
VL30-5 0.051293559013285944 0.037437571134660785
RT-VL30-5 -7.901738501286278 4.978419726722227 -5.046517047455799 -41.18633541523535
VL30-all 0.02242916316335144 0.01627342507926177
RT-VL30-all -10.44767837784461 4.645938368194949 -6.512908730294181 -19.369445133821035
*** budget = 40
VL40-1 0.017966044797249402 0.01721333615302921
RT-VL40-1 -8.137581758131779 1.8638308202556169 -6.466851117385538 -18.237137914952797
VL40-2 0.014790879560417577 0.013169925528020389
RT-VL40-2 -6.174359678909797 1.7856990263520485 -4.928448482866899 -14.040779423131212
VL40-3 0.0008467101864520652 0.0001797259137664495
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VL40-4 0.013605201384925249 0.015411324205044694
RT-VL40-4 -10.051157272306105 7.497029893191314 -5.661074005849526 -35.975250251471
VL40-5 0.04028882209329795 0.030063393091401245
RT-VL40-5 -8.042572188505286 5.638675785167759 -5.046517047455799 -41.18633541523535
VL40-all 0.01749953160446845 0.012801472262815921
RT-VL40-all -10.310712249272777 4.58561781217877 -6.174359678909797 -19.147890348510924
*** budget = 50
VL50-1 0.012511497807280088 0.010803317374719599
RT-VL50-1 -7.8372860368898465 0.9005194530268332 -5.473470257689373 -10.227886579627182
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RT-VL50-all -10.068807192526545 4.668960689508303 -6.000034133638363 -18.949059130924915
logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00020_20_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=3_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s3/04.09_10.40.40/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00020_20_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=3_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s3/04.09_10.40.40/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00020_20_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=3_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s3/04.09_10.40.40/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
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LR -0.08344882028359145 0.08214587985345098 -0.0001226436405321607 -0.21251945426932672
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.07700036236626102 0.07387929039382499
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VL10-all 0.05355808022071999 0.02916527817425045
RT-VL10-all -8.616028972867541 5.485911546259825 -5.0577314960558235 -19.547086942494825
*** budget = 20
VL20-1 0.03680109313105722 0.03619227499498979
RT-VL20-1 -4.671578753384711 1.1071795097665678 -3.3686782698321567 -9.409801051978341
VL20-2 0.022357220342390485 0.01939484037914128
RT-VL20-2 -4.941792101169829 1.3643270457213663 -3.1673408051449874 -15.874909985009635
VL20-3 0.0008778993971193453 7.947694485829226e-05
RT-VL20-3 -19.88556624876215 0.3855308686944996 -18.098702909440508 -20.000024138165212
VL20-4 0.03623219564310218 0.02989947221763782
RT-VL20-4 -4.807662853278769 0.782543855313831 -3.844570264188272 -8.997861794462656
VL20-5 0.0398405196099376 0.028599988017465182
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VL20-all 0.027221785624721363 0.014490323704795758
RT-VL20-all -8.061092107618403 5.930845180978164 -4.671578753384711 -19.88556624876215
*** budget = 30
VL30-1 0.020945809842389175 0.025974066184322644
RT-VL30-1 -4.765377991372572 1.0832097636033617 -3.5144089997445267 -9.409801051978341
VL30-2 0.01619307578570092 0.016251313873116787
RT-VL30-2 -5.0309455502064715 1.639213770404926 -3.1673408051449874 -15.874909985009635
VL30-3 0.0008666961439272481 9.538521111657365e-05
RT-VL30-3 -19.828337304060625 0.46165530403056226 -18.098702909440508 -20.000024138165212
VL30-4 0.026512564546475322 0.021061951919217746
RT-VL30-4 -4.750103622086893 0.8065497725860831 -3.854696208988705 -8.997861794462656
VL30-5 0.03012641790608213 0.023318347151672105
RT-VL30-5 -6.0138960805843 1.414371842100035 -4.401955644379591 -13.970991153110992
VL30-all 0.018928912844914957 0.010204824768626579
RT-VL30-all -8.077732109662174 5.893451468955969 -4.750103622086893 -19.828337304060625
*** budget = 40
VL40-1 0.013165889843952693 0.01466003909730952
RT-VL40-1 -5.151544828974044 1.2778499245792494 -3.5144089997445267 -9.409801051978341
VL40-2 0.0140746336714238 0.013766627557365884
RT-VL40-2 -5.110964526886487 1.8970854553778804 -3.1673408051449874 -15.874909985009635
VL40-3 0.0008548873094815239 0.00010843759204782325
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VL40-4 0.021232265340762547 0.016839587348667178
RT-VL40-4 -4.837634973839286 0.907110973647119 -3.876763008252717 -8.997861794462656
VL40-5 0.02682258971636926 0.025480372383660146
RT-VL40-5 -6.156662678996659 1.8293128063864823 -4.622258615901874 -13.970991153110992
VL40-all 0.015230053176397964 0.008748195385260454
RT-VL40-all -8.204964382317044 5.798875591499777 -4.837634973839286 -19.76801490288874
*** budget = 50
VL50-1 0.010149541445996505 0.01250442173514841
RT-VL50-1 -5.1780788450846424 1.4121314607640618 -3.5435808732917318 -9.409801051978341
VL50-2 0.009950072014157459 0.010355254835584247
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VL50-3 0.0008442896375430534 0.00011793544284364432
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VL50-4 0.015740576563893816 0.011903472905857726
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VL50-5 0.02296514955973365 0.02000190193969414
RT-VL50-5 -5.92927192960384 1.6081663364892396 -4.5196826359948155 -13.970991153110992
VL50-all 0.011929925844264897 0.007297645390938412
RT-VL50-all -8.096816179756857 5.822377799759353 -4.804706201394193 -19.71387941465757
logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00012_12_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=2_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s2/04.09_10.40.36/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00012_12_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=2_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s2/04.09_10.40.36/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00012_12_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=2_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s2/04.09_10.40.36/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -9.064880561151801 7.184206191767482 -3.0892818678964344 -34.741443649181925
LR -0.06687366643052904 0.08739773915056759 -5.696673116761808e-05 -0.46345971664721575
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.1387565767447211 0.08040952340117094
RT-VL10-1 -6.612373086489938 5.053407552054157 -3.792859837418942 -30.067620046087814
VL10-2 0.05230060431208407 0.044723457481232166
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VL10-3 0.005042791168917646 0.012143611355850611
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VL10-5 0.026144389874415083 0.042988801430062354
RT-VL10-5 -4.494180251975096 2.7361075340274845 -3.127253003632947 -20.20174070432644
VL10-all 0.0522439271938555 0.045977455336265974
RT-VL10-all -8.644434563950611 5.006173588569334 -4.494180251975096 -18.498083243422023
*** budget = 20
VL20-1 0.09490701462366792 0.04711961687133772
RT-VL20-1 -5.427062321135015 1.6179893055715 -3.8289963668650087 -10.623305721024543
VL20-2 0.02741173267154095 0.023143240442611
RT-VL20-2 -6.00357999310218 5.291853428665334 -3.7219761430679985 -28.501092837950267
VL20-3 0.0012932507855027678 0.0023369660120409197
RT-VL20-3 -19.152901818301448 3.74133034371822 -5.159526310439889 -23.678324961166975
VL20-4 0.020333027434579664 0.02002574106919928
RT-VL20-4 -7.024438399914426 5.38476283223218 -4.10856641122875 -22.298441365132156
VL20-5 0.008333834096129334 0.009166568121236205
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VL20-all 0.030455771922284124 0.033482925764973324
RT-VL20-all -8.334536595490139 5.493116591999291 -4.064700444997627 -19.152901818301448
*** budget = 30
VL30-1 0.07764909364288969 0.03538574592250503
RT-VL30-1 -5.843524365723869 1.7267344948036378 -3.9379992416243006 -10.623305721024543
VL30-2 0.020834599889590508 0.01752213657251443
RT-VL30-2 -6.18765783912443 5.52102708598284 -3.7219761430679985 -28.501092837950267
VL30-3 0.0009284750389808161 0.0008253197340601907
RT-VL30-3 -19.296201259167766 3.6076693091813414 -5.159526310439889 -23.678324961166975
VL30-4 0.01370978878371033 0.012840170595709972
RT-VL30-4 -7.865654689510858 5.945299441329824 -4.10856641122875 -21.60874429768002
VL30-5 0.005522505444993771 0.005871777398439887
RT-VL30-5 -3.9798003347673 1.6257181326992747 -3.2901936616428893 -14.534339191474956
VL30-all 0.023728892560033025 0.027811792557386767
RT-VL30-all -8.634567697658845 5.471851740556655 -3.9798003347673 -19.296201259167766
*** budget = 40
VL40-1 0.07084955164106829 0.0356064992480701
RT-VL40-1 -6.160513958003534 1.8854982388720827 -3.7219761430679985 -10.623305721024543
VL40-2 0.016591680608471243 0.014221780375811443
RT-VL40-2 -6.2778912429395035 5.553645832507165 -4.041540223358769 -28.501092837950267
VL40-3 0.0007863853486039286 0.00023209849174268306
RT-VL40-3 -19.450006675404488 3.4644863819249117 -5.165371766198551 -23.678324961166975
VL40-4 0.00971876709990239 0.008171372803441566
RT-VL40-4 -7.249343201826304 5.390722282147234 -4.2567444720895224 -21.535729853892708
VL40-5 0.003868132308472977 0.004878859422765222
RT-VL40-5 -3.970438578185727 1.8113915091281405 -3.2901936616428893 -14.534339191474956
VL40-all 0.020362903401303766 0.025813713184717448
RT-VL40-all -8.621638731271911 5.5194328994269535 -3.970438578185727 -19.450006675404488
*** budget = 50
VL50-1 0.0672705254021271 0.033583684359667174
RT-VL50-1 -6.397241183753298 1.9024279322591267 -4.0648882085129525 -10.623305721024543
VL50-2 0.014993400957239418 0.012296892381930958
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VL50-3 0.0007598038857940193 0.0002504087851499066
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VL50-4 0.008951422857774375 0.008010007256098671
RT-VL50-4 -6.533339513220109 4.64994232266238 -4.461978539632873 -21.425378909809837
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VL50-all 0.018956810370061493 0.02466543147162427
RT-VL50-all -8.625620547282823 5.4279006479738765 -4.16533722356868 -19.32166926742698
logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00004_4_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=1_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s1/04.09_10.40.33/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00004_4_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=1_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s1/04.09_10.40.33/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/l2/run_2024-04-09_10-39-57/run_0931b_00004_4_dl_indist=2,dl_scale=0.1000,dr_cc=0.7000,seed=1_2024-04-09_10-39-57/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.100_zt=p_g1_s1/04.09_10.40.33/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -10.416364562709653 6.492966104215101 -3.7010703541106977 -49.209838541895216
LR -0.07587247910063542 0.0844530519455964 -0.0003975224700936142 -0.5917962391109957
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.09493247809017066 0.059959912463666665
RT-VL10-1 -8.50555148151421 4.6772338612099364 -4.409212837089024 -26.802083665386174
VL10-2 0.031670772494403136 0.028245181611462403
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VL10-4 0.04048806854185183 0.03605858075094409
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VL10-5 0.0780824097332775 0.06834792662253483
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VL10-all 0.0498113583801876 0.03274318460664104
RT-VL10-all -9.761221066815292 4.485273549114929 -6.523981955514028 -18.57096209467643
*** budget = 20
VL20-1 0.06663213865865156 0.036339379348952226
RT-VL20-1 -8.582902564201563 4.322995145384014 -4.937350071893282 -23.41038731406992
VL20-2 0.01690600462014685 0.014261576841529258
RT-VL20-2 -8.519913637800796 5.162642434423539 -4.914881442204709 -22.870702324515246
VL20-3 0.0008599560700522657 0.00033965270877628047
RT-VL20-3 -19.851129045072586 1.6701626322521286 -5.730370439585736 -22.06513425246207
VL20-4 0.019848291706809622 0.017346617035780505
RT-VL20-4 -6.416104416903634 2.1472969652876155 -4.988913588245394 -21.008389880179116
VL20-5 0.04221735431817089 0.0422789422740875
RT-VL20-5 -6.697686224337471 1.3362135418083245 -4.3441565404976075 -11.33976601432952
VL20-all 0.029292749074766238 0.02285750802866801
RT-VL20-all -10.01354717766321 4.999844606348126 -6.416104416903634 -19.851129045072586
*** budget = 30
VL30-1 0.050647112106096806 0.024874521217590703
RT-VL30-1 -8.872124482655694 4.411187572734688 -4.958713916383959 -23.41038731406992
VL30-2 0.011920718978244295 0.012250742722368159
RT-VL30-2 -9.391832522033921 5.992976433822485 -4.995301935888613 -24.129744897231795
VL30-3 0.0007927679809267984 0.0002311098953117036
RT-VL30-3 -19.735379296240342 2.015462468600387 -5.730370439585736 -21.18072397051589
VL30-4 0.013323445383419907 0.014221268657897078
RT-VL30-4 -6.558968943894883 2.0280527455134543 -4.988913588245394 -19.777545793600034
VL30-5 0.03144317722278133 0.030263973347373824
RT-VL30-5 -6.869204929453434 1.23771518994629 -4.643802418001408 -10.32183087436797
VL30-all 0.02162544433429383 0.017525697499314524
RT-VL30-all -10.285502034855654 4.85086788830309 -6.558968943894883 -19.735379296240342
*** budget = 40
VL40-1 0.04468013129732538 0.020307762960944917
RT-VL40-1 -9.344575069057615 4.426506072326148 -5.296463975025489 -23.41038731406992
VL40-2 0.008014186046525422 0.006445394947696097
RT-VL40-2 -8.617056560831339 5.446570315746031 -4.925293654793052 -22.870702324515246
VL40-3 0.0007690541515310897 0.0002515038391250815
RT-VL40-3 -19.639369756605834 2.335282301820771 -5.730370439585736 -21.18072397051589
VL40-4 0.008774538679582429 0.008144754050523789
RT-VL40-4 -6.388537313901938 1.7130766957876367 -4.988913588245394 -15.701971266230277
VL40-5 0.023445619044975302 0.023370247207258513
RT-VL40-5 -7.198263776399727 1.1718512937939485 -5.251018597978021 -10.32183087436797
VL40-all 0.017136705843987927 0.015617227030092714
RT-VL40-all -10.23756049535929 4.813933471819973 -6.388537313901938 -19.639369756605834
*** budget = 50
VL50-1 0.039161912820232986 0.01723111554102495
RT-VL50-1 -9.915972274951084 4.762341168987183 -4.987342172233829 -23.41038731406992
VL50-2 0.007714719107197232 0.006498639763143527
RT-VL50-2 -8.18755813859463 5.279616032950416 -5.06317152582794 -22.870702324515246
VL50-3 0.0007607929227926931 0.00025528054301984966
RT-VL50-3 -19.548903050162142 2.5848443105928487 -5.730370439585736 -21.18072397051589
VL50-4 0.007599119124561238 0.006652537374520212
RT-VL50-4 -6.0821618114109635 0.5886780975279818 -4.988913588245394 -6.769188792197654
VL50-5 0.0173636624493772 0.01474892263046132
RT-VL50-5 -7.097743227236704 1.1129776249045784 -5.431812955987481 -10.32183087436797
VL50-all 0.01452004128483227 0.013407732866319128
RT-VL50-all -10.166467700471104 4.86019646910418 -6.0821618114109635 -19.548903050162142
logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00037_37_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=5_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s5/04.08_23.23.29/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s5
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00037_37_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=5_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s5/04.08_23.23.29/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00037_37_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=5_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s5/04.08_23.23.29/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.150594181853019 5.633684541760591 -3.1240226305460492 -34.822079180176694
LR -0.041101665785270344 0.06969425996519586 -9.824660249790895e-07 -0.5111848840557682
VL-std-all 0.0 0.0
*** budget = 10
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RT-VL10-1 -7.948449996757082 6.071558832291846 -3.6274109386890547 -34.822079180176694
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VL10-5 0.07406829879271726 0.06333160238346265
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VL10-all 0.044887877627887575 0.017508344738336908
RT-VL10-all -7.202967069286427 1.3201986936950516 -5.965174450686833 -9.455740740500909
*** budget = 20
VL20-1 0.025513783381614887 0.020244423258093395
RT-VL20-1 -7.89733039418823 4.847508418254911 -4.3581878035296935 -19.661035015776275
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VL20-3 0.01053266431133818 0.012855306192604462
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VL20-4 0.014943337346734052 0.014464193894760568
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VL20-5 0.03934902716305608 0.037611096318543355
RT-VL20-5 -6.112528237280735 3.688141070339711 -3.342600913626174 -26.241148370376305
VL20-all 0.02196284906864824 0.010009977318909572
RT-VL20-all -7.568340611523148 2.4160575900324415 -5.518052426891874 -12.13249252498858
*** budget = 30
VL30-1 0.020189468836829132 0.01848959132544494
RT-VL30-1 -8.141957825405319 5.574958581481563 -4.3581878035296935 -29.606133589721207
VL30-2 0.013934590164924106 0.011137461983628976
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VL30-3 0.007114194444019446 0.0099668057862703
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VL30-4 0.010141497375810726 0.009710966451072512
RT-VL30-4 -6.299572273125614 5.4386381571266105 -3.2660095308157384 -29.555255786454147
VL30-5 0.02747607564973965 0.026631086841404535
RT-VL30-5 -5.57068105870655 2.722256555125997 -3.4351771473351107 -18.325362981301986
VL30-all 0.01577116529426461 0.007301173138663683
RT-VL30-all -7.809819077478702 2.9534080791588013 -5.57068105870655 -13.415197843625053
*** budget = 40
VL40-1 0.01450410071845189 0.012249840539929602
RT-VL40-1 -8.510013572329205 5.96785924078446 -4.3581878035296935 -29.606133589721207
VL40-2 0.011210907209987481 0.009933822781635305
RT-VL40-2 -5.38938063896699 2.489191536542917 -3.736937827375119 -16.733010511349146
VL40-3 0.004765826843639504 0.006261975978250913
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VL40-4 0.007448680539493865 0.006590411942062029
RT-VL40-4 -6.108563151477822 5.460985453909776 -3.2660095308157384 -29.555255786454147
VL40-5 0.02312211288266971 0.02822010826277696
RT-VL40-5 -5.429694181881022 2.179886078116888 -3.24883274437388 -15.673003729992722
VL40-all 0.012210325638848489 0.006378419373641906
RT-VL40-all -7.963834615327445 3.405193974817702 -5.38938063896699 -14.381521531982187
*** budget = 50
VL50-1 0.011420003760945789 0.008391662200858505
RT-VL50-1 -7.994182009898089 4.957680696567631 -4.3581878035296935 -19.661035015776275
VL50-2 0.008401987696892007 0.0076210323912892924
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VL50-3 0.002690454444248036 0.00455803246314102
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VL50-4 0.007110672273672233 0.006562138966686366
RT-VL50-4 -7.107046825732666 6.758190986881341 -3.2361768992185573 -29.555255786454147
VL50-5 0.017507802881414177 0.017485901767538975
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VL50-all 0.009426184211434449 0.00492074194108693
RT-VL50-all -8.410674324566122 3.570342273562038 -5.612332783445444 -15.350975785397775
logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00029_29_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=4_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s4/04.08_23.23.30/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s4
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00029_29_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=4_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s4/04.08_23.23.30/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00029_29_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=4_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s4/04.08_23.23.30/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -6.707994308763382 3.950883841385166 -4.198782527548318 -38.23033329088446
LR -0.03365575257854028 0.05122619278145308 -2.0229108010028085e-05 -0.4571985293862755
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.04559811129634875 0.04663915177422317
RT-VL10-1 -6.351535795316196 3.3745033047194224 -4.374046355417656 -22.560772373649165
VL10-2 0.028231490637828514 0.028980094893059166
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VL10-3 0.019404831495743402 0.02598065278858011
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VL10-4 0.049844824993181554 0.045357816531465506
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VL10-5 0.09569906541217355 0.08381911585035845
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VL10-all 0.04775566476705515 0.026429348896459044
RT-VL10-all -6.6956578747155735 1.5928565160374182 -5.62984695306945 -9.84446525960202
*** budget = 20
VL20-1 0.018526102608363796 0.017838837259124492
RT-VL20-1 -5.986772407055313 2.0214069371902963 -4.992309368758448 -21.720477523859024
VL20-2 0.012883927363352659 0.012524910627487215
RT-VL20-2 -5.6541966041832366 2.557853055982508 -4.696395486094869 -20.08732891932146
VL20-3 0.007050449040287518 0.010945204524927822
RT-VL20-3 -11.726474392578245 7.142524721710789 -5.152594283256574 -20.000024138165212
VL20-4 0.024387060264489495 0.022903141055056527
RT-VL20-4 -5.819805534030917 2.322056993373301 -5.109155685752033 -24.428077205068128
VL20-5 0.044692024239137956 0.03924038192292952
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VL20-all 0.021507912703126287 0.012946621864706596
RT-VL20-all -6.9299033010982 2.4045910003173256 -5.462267567643293 -11.726474392578245
*** budget = 30
VL30-1 0.014730534677914645 0.015152556213867428
RT-VL30-1 -6.3805616996723655 3.2241510324600844 -4.98483999408473 -22.509809089663527
VL30-2 0.008765481109865808 0.00973039979047299
RT-VL30-2 -5.782597239871209 2.94422402343228 -4.696395486094869 -20.08732891932146
VL30-3 0.004317425333490698 0.007194748431622502
RT-VL30-3 -13.402383730839393 7.15378365214655 -5.157453720136063 -20.000024138165212
VL30-4 0.015762505986545227 0.013429256277242169
RT-VL30-4 -5.650215580371099 1.0114341004436296 -5.109155685752033 -12.217859825638572
VL30-5 0.03181446746158469 0.028247461938874747
RT-VL30-5 -5.506081151482327 0.28768682645443483 -4.812122591553879 -6.282208968023889
VL30-all 0.015078082913880214 0.009341696693275805
RT-VL30-all -7.34436788044728 3.0435852557537952 -5.506081151482327 -13.402383730839393
*** budget = 40
VL40-1 0.010086992605412595 0.00916835029980831
RT-VL40-1 -6.24147981117754 2.817480376444687 -4.992309368758448 -21.720477523859024
VL40-2 0.006388064945566802 0.00668831779657808
RT-VL40-2 -5.988347958417832 3.3910572005276416 -4.735480657929534 -20.08732891932146
VL40-3 0.002943368188099431 0.0058796394041019315
RT-VL40-3 -14.96833150051409 6.842502431940707 -5.200744004996953 -20.000024138165212
VL40-4 0.013428715348968912 0.01259599427582609
RT-VL40-4 -5.987243924273151 3.095205270098931 -5.147718159031092 -24.428077205068128
VL40-5 0.025592347960819214 0.02509741052742685
RT-VL40-5 -5.500069657557813 0.3255946911625025 -4.795038770399271 -6.282208968023889
VL40-all 0.01168789780977339 0.007790790766974762
RT-VL40-all -7.737094570388086 3.6235900293713184 -5.500069657557813 -14.96833150051409
*** budget = 50
VL50-1 0.007699245592907657 0.00721164245943028
RT-VL50-1 -5.832400913787028 1.2328405516534708 -5.10511702488316 -11.51516308837658
VL50-2 0.004657106288391551 0.004650265214145661
RT-VL50-2 -6.127163081259321 3.7428410746490814 -4.735480657929534 -20.08732891932146
VL50-3 0.0013501306260942111 0.001572682381172807
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VL50-4 0.011805079637796942 0.011938386215697993
RT-VL50-4 -5.4858258409612235 0.37081756945876126 -5.147718159031092 -6.398273686341149
VL50-5 0.0192230739476617 0.01872666270637436
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VL50-all 0.008946927218570412 0.006187710697322866
RT-VL50-all -7.624592209641674 3.7964210954871893 -5.475566051665456 -15.202005160535341
logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00021_21_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=3_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s3/04.08_23.23.26/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s3
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00021_21_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=3_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s3/04.08_23.23.26/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00021_21_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=3_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s3/04.08_23.23.26/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.050921066745308 4.47244824653498 -3.6853872254099627 -43.98325845296383
LR -0.04267250064486509 0.0566548138135638 -3.2792467050457574e-05 -0.7047588067724367
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.040973596750947174 0.045297224583463865
RT-VL10-1 -7.231001626357487 2.573917344520429 -3.7466145593375195 -26.61615260369093
VL10-2 0.03568776156847796 0.03217665431006351
RT-VL10-2 -5.051417086145588 2.286677864645473 -3.691289499900911 -22.263651624145538
VL10-3 0.020183455691578988 0.02550335765498821
RT-VL10-3 -11.523984265448316 6.9829105991150575 -3.715835822669967 -21.565000642523334
VL10-4 0.042716590776235394 0.037990317475995204
RT-VL10-4 -5.778521150356356 1.8757236372131894 -3.7449149185551334 -21.565000642523334
VL10-5 0.058913543766717306 0.06228555029844753
RT-VL10-5 -6.187148654901578 2.3455422288690237 -3.9983693628949126 -21.565000642523334
VL10-all 0.03969498971079137 0.012464955404090353
RT-VL10-all -7.154414556641865 2.2956606617882205 -5.051417086145588 -11.523984265448316
*** budget = 20
VL20-1 0.020510013150822435 0.02212442979293363
RT-VL20-1 -7.44207456757751 1.8714829415118688 -3.8747627120256496 -14.074022319111691
VL20-2 0.01992007342409083 0.01763456769755859
RT-VL20-2 -4.661891054801461 0.8509555076252432 -3.7073363974672002 -6.954963619683734
VL20-3 0.00812533823257103 0.012468369477806064
RT-VL20-3 -14.084270458499061 6.811124741212863 -3.910809905599976 -20.000024138165212
VL20-4 0.021014623505668625 0.018183207441529604
RT-VL20-4 -5.565999973175119 1.3280383761075503 -3.7449149185551334 -10.06816492424377
VL20-5 0.030460789588501454 0.02924998832482436
RT-VL20-5 -6.1114211945607275 1.8586353800715762 -4.101841218627661 -14.141935923344029
VL20-all 0.020006167580330875 0.007095512430266157
RT-VL20-all -7.5731314497227755 3.3779461620689504 -4.661891054801461 -14.084270458499061
*** budget = 30
VL30-1 0.013988678518448401 0.011564414430290264
RT-VL30-1 -7.678128676529603 1.7456086768318455 -3.8747627120256496 -11.316113011429943
VL30-2 0.013607781323948876 0.013250271777156737
RT-VL30-2 -4.800651060207671 0.7955156647154896 -3.721517575740794 -6.948684546554102
VL30-3 0.004669218221640809 0.007756832331259434
RT-VL30-3 -15.53849475113733 6.489413009310668 -4.0754230150505375 -20.07164910983424
VL30-4 0.015825035780958067 0.01414088576955402
RT-VL30-4 -5.724943307003164 1.4130305836128412 -3.7449149185551334 -10.06816492424377
VL30-5 0.019642299935181615 0.01590235587037498
RT-VL30-5 -6.2372476876841825 1.8516473796086717 -4.680818241489386 -14.141935923344029
VL30-all 0.013546602756035553 0.004926579160376958
RT-VL30-all -7.99589309651239 3.8846217895180257 -4.800651060207671 -15.53849475113733
*** budget = 40
VL40-1 0.01068554020539401 0.010055912556912508
RT-VL40-1 -7.911459078488547 1.5102048028785282 -3.922782484190244 -10.530428193207062
VL40-2 0.00886106029980693 0.007965858411743335
RT-VL40-2 -4.740705445260068 0.7786158178998689 -3.7356712749459384 -6.948684546554102
VL40-3 0.0021527079719209684 0.005193367069264641
RT-VL40-3 -17.180804156573775 5.5343249254918465 -4.7560296732637655 -20.000024138165212
VL40-4 0.011214118730780358 0.008956258997272015
RT-VL40-4 -5.905192236383714 1.5637979647010056 -3.7449149185551334 -10.06816492424377
VL40-5 0.016684022556722154 0.014092261082835664
RT-VL40-5 -6.028277818658578 1.6319412519250893 -4.680818241489386 -14.141935923344029
VL40-all 0.009919489952924885 0.004678995056219708
RT-VL40-all -8.353287747072937 4.529235083685616 -4.740705445260068 -17.180804156573775
*** budget = 50
VL50-1 0.009532768537270477 0.009627461572687922
RT-VL50-1 -7.816746616109119 1.6896257581150795 -3.922782484190244 -10.530428193207062
VL50-2 0.008229104954680674 0.007267482399465612
RT-VL50-2 -4.807618763721831 0.8681466460129242 -3.7356712749459384 -6.954963619683734
VL50-3 0.00215945877594292 0.005114859889273359
RT-VL50-3 -15.934497870332022 6.42363480683127 -3.854704949351807 -20.000024138165212
VL50-4 0.009224953783210406 0.007859292945091693
RT-VL50-4 -5.987446573492144 1.633915607198516 -3.7449149185551334 -10.06816492424377
VL50-5 0.010892795062116675 0.009101956826181777
RT-VL50-5 -5.902670752353512 0.9666678248456222 -4.758756904427452 -8.77786616152525
VL50-all 0.00800781622264423 0.0030457268372177325
RT-VL50-all -8.089796115201725 4.0395539548521215 -4.807618763721831 -15.934497870332022
logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00013_13_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=2_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s2/04.08_23.23.20/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s2
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00013_13_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=2_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s2/04.08_23.23.20/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00013_13_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=2_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s2/04.08_23.23.20/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
RT -7.541586696537737 5.842926088609815 -3.4860306754056194 -28.7310583886703
LR -0.04763750035517347 0.07141748082152131 -4.879722100725736e-05 -0.2994973633796709
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.042761330379492646 0.05622463550186786
RT-VL10-1 -7.3958017770278035 3.491539787899387 -3.4860306754056194 -24.27350147456313
VL10-2 0.036496091714927674 0.03327015634222518
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VL10-4 0.03682830258775332 0.03654499688207168
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VL10-5 0.10654451869806332 0.07698210643302689
RT-VL10-5 -5.365607956354991 2.9571786808526035 -3.621605584757515 -21.73549359947617
VL10-all 0.04633444681630651 0.03229831948991232
RT-VL10-all -7.60490932076277 3.616380741029862 -5.21460001134193 -14.656367098926415
*** budget = 20
VL20-1 0.01542580482979606 0.019305332524632304
RT-VL20-1 -7.860486101681965 2.548194229647177 -3.7320135445268745 -22.571688402225643
VL20-2 0.01847089636111745 0.020081417667813392
RT-VL20-2 -4.812198109761731 2.5821637208973507 -3.7445635336499787 -19.742742310855885
VL20-3 0.0027176093695010003 0.005813984794784118
RT-VL20-3 -16.528734412083587 6.572695961183625 -4.410199992865428 -23.282718426035093
VL20-4 0.017793466309891936 0.01582041627888934
RT-VL20-4 -4.751393721632757 0.9754090358320268 -3.6917868700035434 -12.863931011770433
VL20-5 0.06412220429093737 0.04612276626488441
RT-VL20-5 -5.109158017635122 1.7332118522373814 -3.7285080683038316 -19.076599123600683
VL20-all 0.023705996232248762 0.02099956241138517
RT-VL20-all -7.812394072559033 4.508997358390993 -4.751393721632757 -16.528734412083587
*** budget = 30
VL30-1 0.00955394410745707 0.011390501327232832
RT-VL30-1 -7.682769521838326 1.928610850499261 -3.5890318489076662 -18.30408994190401
VL30-2 0.011178159206114636 0.011473197153389873
RT-VL30-2 -4.494349417199277 1.746999167184544 -3.7539095050244033 -16.488089955335973
VL30-3 0.0015098142076082287 0.0033455517630880133
RT-VL30-3 -18.08121063193662 5.3346445992069675 -4.791628734545792 -23.160771905698642
VL30-4 0.014013073347571705 0.01209944610405276
RT-VL30-4 -4.828249760973472 1.166568212184395 -3.9407582208784646 -12.863931011770433
VL30-5 0.047364963011462945 0.04056540950921404
RT-VL30-5 -5.196694552969591 2.0886154646606583 -3.7378352338002188 -19.076599123600683
VL30-all 0.01672399077604292 0.01587359592860414
RT-VL30-all -8.056654776983457 5.136603959422561 -4.494349417199277 -18.08121063193662
*** budget = 40
VL40-1 0.006600512125895048 0.007520335575155374
RT-VL40-1 -7.998272098824821 1.9790621671966966 -4.834284168340574 -18.30408994190401
VL40-2 0.007678162538725715 0.0066490494843283905
RT-VL40-2 -5.024368582000003 3.2586493277203674 -3.793061710656184 -20.426530585461133
VL40-3 0.0007935605623167197 0.00023579580091389235
RT-VL40-3 -18.0693791077425 5.200436911489697 -4.912688821161771 -21.902063662472692
VL40-4 0.010830238701789632 0.013097545317123364
RT-VL40-4 -4.899037777939188 1.3376343311925256 -4.086350565600543 -12.863931011770433
VL40-5 0.0329284555571735 0.027342059031175148
RT-VL40-5 -4.982793849669637 0.7185504207397018 -3.7378352338002188 -7.557793536011932
VL40-all 0.011766185897180124 0.011068082774760343
RT-VL40-all -8.19477028323523 5.074970015220864 -4.899037777939188 -18.0693791077425
*** budget = 50
VL50-1 0.005879023645221559 0.008098040634247938
RT-VL50-1 -8.1028518785217 2.055711886082187 -5.514868793566009 -18.30408994190401
VL50-2 0.006084217901557417 0.005432024921922536
RT-VL50-2 -4.600213427946363 2.226129252470144 -3.7539095050244033 -16.488089955335973
VL50-3 0.0007643622955887941 0.0002543578880135175
RT-VL50-3 -17.514492286327304 5.665790606328822 -4.912688821161771 -21.902063662472692
VL50-4 0.009019208641581311 0.008868328177070344
RT-VL50-4 -4.983680859680943 1.4692607877922568 -4.406669012072747 -12.863931011770433
VL50-5 0.029644395424790627 0.025632771256663908
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VL50-all 0.01027824158174794 0.010040816010379676
RT-VL50-all -8.045629896233821 4.899422812157071 -4.600213427946363 -17.514492286327304
logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00005_5_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=1_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s1/04.08_23.23.20/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type2-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s1
v_data: 0
Inferred from ckptpath name:
il_method: infogsdr
rl_method: ppobc
activation: tanh
hidden_size: [32, 32]
norm_obs: 1
info_loss_type: None
encode_sampling: normal
normalize_code: 0
tl_emb: 0
TRAINED C_DATA: 2
ARG C_DATA: 2
Using (32, 32) tanh networks.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
args.encode_dim: 2
TRAJ is loaded from /home/vsreeramdass3/code/vild_code1/imitation_data/STRAT_h5/FetchPickPlaceWide_v0.h5 with traj_num 25.0, data_size 2500 steps, and average return -0.01
No of trajs: Train 10 Val 4 Test 11
Total data pairs: 990, K 10, state dim 34, action dim 4, a min -0.549648, a_max 0.5452624
Policy model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00005_5_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=1_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s1/04.08_23.23.20/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2d2/cgrew_pr/rr/run_2024-04-08_23-22-44/run_6e30f_00005_5_dl_indist=2,dl_scale=0.5000,dr_cc=0.7000,seed=1_2024-04-08_23-22-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type2-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.70_reg01_ds0.500_zt=n_g1_s1/04.08_23.23.20/models/ckpt_discr_T10000000.pt
***** Max Ep Steps: 1000 args.seed 1 test_seed 1 *****
*** budgets = [10, 20, 30, 40, 50] NPARALLEL = 50 n_test_episodes = 1 ***
***** num zs = 1500 *****
***** args.encode_sampling = normal *****
obj_pos_y_last [0.43 0.59 0.75 0.91 1.07]
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
State dim: 34, action dim: 4, action bound 1
Use state-normalized environments.
zvs shape 1500
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LR -0.06413812301938268 0.09159651516802146 -1.797703225459024e-05 -1.175202340716926
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.029902827872141297 0.02799389641615384
RT-VL10-1 -7.916607891489716 2.677258175673846 -4.878514052990061 -24.0015724754712
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VL10-5 0.134525842393911 0.07268364310230299
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RT-VL10-all -8.693813103315097 1.1605756232243456 -6.858393319345256 -9.995150021066793
*** budget = 20
VL20-1 0.014266167202644287 0.011362489630450275
RT-VL20-1 -7.652746162331006 1.1394597884084794 -5.506213828249894 -13.937923047836282
VL20-2 0.016644584718781157 0.017577399702507462
RT-VL20-2 -6.412958933527764 1.8205809608039565 -4.873980376175933 -19.653465468681247
VL20-3 0.01583504604278606 0.015083973195104322
RT-VL20-3 -10.40913435126509 6.299969217720106 -5.097726398874446 -28.20325173133044
VL20-4 0.025898247312517077 0.022706868869670115
RT-VL20-4 -9.278308295937537 6.317782580677803 -4.772336508814516 -25.579483648276497
VL20-5 0.09970535324271199 0.053903559826416175
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VL20-all 0.034469879703888116 0.032870436733950854
RT-VL20-all -9.005581190670265 1.7745624067395047 -6.412958933527764 -11.274758210289926
*** budget = 30
VL30-1 0.010350948235395072 0.009172444567495547
RT-VL30-1 -7.410630073998907 0.7911477884764355 -5.605585787641673 -10.040680752582755
VL30-2 0.010023798799911627 0.012243994939495476
RT-VL30-2 -6.210078285254134 0.9073246066482328 -4.95667085680641 -9.569615561049671
VL30-3 0.010738619708408026 0.009521734505202056
RT-VL30-3 -10.063204587856823 5.871132986976656 -5.097726398874446 -21.9449561768747
VL30-4 0.01725621225627632 0.015489058411448157
RT-VL30-4 -9.574151070525977 6.723944204154244 -4.772336508814516 -25.579483648276497
VL30-5 0.0790167700560983 0.036528419597952604
RT-VL30-5 -11.462199088389662 9.546208911796253 -4.831768987686707 -42.16214607754377
VL30-all 0.025477269811217874 0.0269031858758726
RT-VL30-all -8.944052621205099 1.8876751409291768 -6.210078285254134 -11.462199088389662
*** budget = 40
VL40-1 0.007134749180746888 0.00639133034936172
RT-VL40-1 -7.48982320266116 0.6167019513313006 -5.961203689763861 -8.578872471212707
VL40-2 0.006245018400286234 0.006868920198182995
RT-VL40-2 -6.4856668547025125 0.9444948250033514 -5.178767355761926 -9.569615561049671
VL40-3 0.008586454675779181 0.008075032633133932
RT-VL40-3 -10.925834836006112 6.295868826231973 -5.097726398874446 -23.07338862686392
VL40-4 0.01269693156611928 0.011402767872375931
RT-VL40-4 -10.36478096551691 6.823482958051648 -4.772336508814516 -25.579483648276497
VL40-5 0.06467909473744522 0.02689860554853646
RT-VL40-5 -12.080402870117597 8.782089230453197 -4.857493189002771 -29.1518053243649
VL40-all 0.01986844971207536 0.022514231064085156
RT-VL40-all -9.469301745800857 2.1242237746218815 -6.4856668547025125 -12.080402870117597
*** budget = 50
VL50-1 0.006366597035281794 0.0064257156856189586
RT-VL50-1 -7.432819161456793 0.6668043583016883 -5.961203689763861 -8.578872471212707
VL50-2 0.004746965295600314 0.005709560685866041
RT-VL50-2 -6.315665517929974 0.9989469514481041 -5.017357626880774 -9.569615561049671
VL50-3 0.006794325573591283 0.006926762747886619
RT-VL50-3 -10.100498280007189 5.622498570343744 -5.097726398874446 -20.133939276278024
VL50-4 0.010888455248104755 0.009899550719230358
RT-VL50-4 -9.453863461227904 6.370327428226543 -4.772336508814516 -25.579483648276497
VL50-5 0.06378780145065709 0.030706065318605383
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VL50-all 0.018516828920647047 0.02272578888638687
RT-VL50-all -9.367675409023576 2.4889318155305764 -6.315665517929974 -13.535530624496015