runf0.log

logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00001_1_clip_discriminator=10,dr_cc=0.9000,seed=1_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.22/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00001_1_clip_discriminator=10,dr_cc=0.9000,seed=1_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.22/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00001_1_clip_discriminator=10,dr_cc=0.9000,seed=1_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s1/08.10_20.43.22/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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*** budget = 20
VL20-1 0.04392451784693805 0.04330009106862689
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VL20-4 0.020174351723096475 0.01883710933395663
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RT-VL20-all -7.9147123797267955 1.9934306225998395 -5.584808497607506 -10.869823696548744
*** budget = 30
VL30-1 0.024293472914897262 0.01722878565250264
RT-VL30-1 -5.688506986629733 0.8143564983834546 -4.8469651208342555 -9.640295724295193
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VL30-3 0.015055065298582672 0.019254446128290924
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VL30-4 0.015623946986003228 0.014328602017772585
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VL30-all 0.01514246130879178 0.005772739114659466
RT-VL30-all -8.13608113008315 2.3671971932757323 -5.6615835471361695 -12.025670581937279
*** budget = 40
VL40-1 0.02072675257002355 0.017790680499288267
RT-VL40-1 -5.749984622140521 0.871679319926651 -4.8469651208342555 -9.640295724295193
VL40-2 0.010791027613859654 0.01177158371339912
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VL40-3 0.00791102315313337 0.008580614885248949
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VL40-4 0.010763375263013342 0.009458095935921135
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VL40-5 0.0041224698082185695 0.003695215103516938
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VL40-all 0.010862929681649699 0.005503773876555302
RT-VL40-all -8.352551614969371 2.6967716902886116 -5.686717612709697 -13.051057466681554
*** budget = 50
VL50-1 0.01701827249403802 0.013359288328924895
RT-VL50-1 -5.6693832626638025 0.6124021543444492 -4.8469651208342555 -8.035336393207793
VL50-2 0.0072377872098432165 0.006779724355393883
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RT-VL50-all -8.305677867378579 2.806136871473596 -5.409528570140739 -13.158521247102348
logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00003_3_clip_discriminator=10,dr_cc=0.9000,seed=2_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s2/08.10_20.43.18/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00003_3_clip_discriminator=10,dr_cc=0.9000,seed=2_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s2/08.10_20.43.18/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00003_3_clip_discriminator=10,dr_cc=0.9000,seed=2_2024-08-10_20-42-43/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s2/08.10_20.43.18/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 -0.1449229629977887 0.08344672085410265 -4.6249012272570056e-05 -0.39539971900923776
VL-std-all 0.0 0.0
*** budget = 10
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*** budget = 20
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*** budget = 30
VL30-1 0.02335246158691202 0.025689433997329144
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*** budget = 40
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RT-VL40-3 -19.890941308211076 2.014540298295639 -10.310590536453407 -25.11484148252555
VL40-4 0.047669299275261846 0.036164740400701605
RT-VL40-4 -12.356350007086522 4.460422832062353 -7.643047864122699 -25.570310992039396
VL40-5 0.11880219622661568 0.08911059534620393
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VL40-all 0.039679026497591155 0.04247325679753612
RT-VL40-all -13.381440714278128 3.280571701395407 -11.275135651878173 -19.890941308211076
*** budget = 50
VL50-1 0.015806813248166577 0.013519604374144574
RT-VL50-1 -11.372295451915676 3.104641775108851 -7.201134313956858 -20.133745128688528
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logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00005_5_clip_discriminator=10,dr_cc=0.9000,seed=3_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s3/08.10_20.43.22/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00005_5_clip_discriminator=10,dr_cc=0.9000,seed=3_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s3/08.10_20.43.22/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00005_5_clip_discriminator=10,dr_cc=0.9000,seed=3_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s3/08.10_20.43.22/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 -7.0770276086310675 2.000168693925299 -4.096128901359522 -34.597503257316745
LR -0.01631278983024167 0.028964273359644063 -4.526394759585273e-05 -0.47165647362849494
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.13391421253359964 0.0928442611134721
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RT-VL10-all -6.8184579439983555 0.45464582432431483 -6.34715410568671 -7.40275354021815
*** budget = 20
VL20-1 0.08214414086510548 0.06324786160979776
RT-VL20-1 -6.245681032240067 1.5038359533350438 -4.72202424728129 -16.496621656957583
VL20-2 0.02466230458710046 0.018975633214451757
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VL20-3 0.010399245405557854 0.010880802788300283
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VL20-4 0.0137755918311638 0.011805917727819424
RT-VL20-4 -7.256355739111856 0.6273607723390502 -5.2552892252695 -9.257521433830371
VL20-5 0.015479830884066358 0.01202084267496599
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VL20-all 0.029292222714598786 0.026844939887092533
RT-VL20-all -6.712971256833693 0.5096913956014772 -6.183252704870679 -7.388007956901385
*** budget = 30
VL30-1 0.06119447492243602 0.05201050436667972
RT-VL30-1 -6.0096506145367705 0.6074396526367827 -4.72202424728129 -7.869627987499174
VL30-2 0.020063714145795383 0.018728400994323997
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VL30-3 0.007530256421046632 0.008873524171008458
RT-VL30-3 -7.160854260056079 2.1474430663548545 -6.008697521255457 -20.989672152881305
VL30-4 0.008938642285547289 0.007858496823215514
RT-VL30-4 -7.336922137320451 0.6242656615575541 -5.779833320891602 -9.257521433830371
VL30-5 0.011480818375528284 0.01060666794964076
RT-VL30-5 -6.467133659139157 0.7394592797437409 -5.608131349668184 -10.514249564303835
VL30-all 0.021841581230070718 0.020151706188089924
RT-VL30-all -6.640329253520518 0.5205209905084166 -6.0096506145367705 -7.336922137320451
*** budget = 40
VL40-1 0.048600801733844484 0.04014621981537321
RT-VL40-1 -6.3751221429995795 1.9355762835445942 -4.72202424728129 -17.28356388805791
VL40-2 0.014679302893008747 0.01650072779261425
RT-VL40-2 -6.300637905333931 1.7368578623325164 -5.130998663715645 -16.496621656957583
VL40-3 0.0040459253145714615 0.0035629877288912066
RT-VL40-3 -6.95802471884385 0.9515257339532656 -6.008697521255457 -12.364906943623236
VL40-4 0.006272209327680029 0.005650483782427693
RT-VL40-4 -7.297231755747881 0.6265833030835902 -6.380765577858871 -9.257521433830371
VL40-5 0.008088139529183965 0.005931326053603915
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VL40-all 0.016337275759657734 0.016517355047988336
RT-VL40-all -6.670847790547896 0.3900396895218835 -6.300637905333931 -7.297231755747881
*** budget = 50
VL50-1 0.04184693889770867 0.040843820255622824
RT-VL50-1 -6.116049532284169 0.6709923565835774 -4.72202424728129 -7.869627987499174
VL50-2 0.010118161873684998 0.009095598436827123
RT-VL50-2 -6.331535967576219 1.918819164195197 -5.130998663715645 -16.496621656957583
VL50-3 0.003810851745489278 0.003516912782861929
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VL50-4 0.005427245359064811 0.004599435892775113
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VL50-5 0.0069212632243980995 0.006375664211650124
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VL50-all 0.01362489222006917 0.014263495522508803
RT-VL50-all -6.585495388191516 0.43398007487593065 -6.116049532284169 -7.257515465305891
logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00007_7_clip_discriminator=10,dr_cc=0.9000,seed=4_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.23/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00007_7_clip_discriminator=10,dr_cc=0.9000,seed=4_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.23/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00007_7_clip_discriminator=10,dr_cc=0.9000,seed=4_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s4/08.10_20.43.23/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 -20.0677909742302 1.2119723650382939 -19.977786319786077 -56.528423651861445
LR -0.20128361996118518 0.019508873986137258 -0.19429906347141124 -0.6929622476402083
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.31290704092284455 0.029256757574709538
RT-VL10-1 -20.581959058236215 3.726655383438822 -20.000024138165212 -56.528423651861445
VL10-2 0.15337241034240243 0.026710012604405683
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VL10-3 0.0008966965699637005 4.4057530589782934e-05
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VL10-4 0.15951892651568395 0.012930384943208344
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VL10-all 0.18923057126666193 0.11823132702830659
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*** budget = 20
VL20-1 0.3067143877491925 0.04043782660965305
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VL20-3 0.000893087236423861 6.209732221389018e-05
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VL20-4 0.15813754712786438 0.018181674607497304
RT-VL20-4 -20.191491019323024 0.9897021728439208 -19.977786319786077 -26.597348502634084
VL20-5 0.31801525806132663 0.01921549694810881
RT-VL20-5 -20.191491019323024 0.9897021728439208 -19.977786319786077 -26.597348502634084
VL20-all 0.18628108135262314 0.11705689085600057
RT-VL20-all -20.54223961537315 0.5123508929037079 -20.000428081605243 -21.16389397830722
*** budget = 30
VL30-1 0.3005217345755406 0.048350593288822435
RT-VL30-1 -21.74582889837823 6.2953944700512885 -20.000024138165212 -56.528423651861445
VL30-2 0.1419178428342142 0.04408473255882437
RT-VL30-2 -21.74582889837823 6.2953944700512885 -20.000024138165212 -56.528423651861445
VL30-3 0.0008894779028840216 7.579600433662793e-05
RT-VL30-3 -20.000630053325267 0.004241406120349325 -20.000024138165212 -20.030319896167708
VL30-4 0.15675616774004478 0.02213899975744365
RT-VL30-4 -20.287224459901932 1.2007376530544642 -19.977786319786077 -26.597348502634084
VL30-5 0.31657273414023823 0.02340107601490648
RT-VL30-5 -20.287224459901932 1.2007376530544642 -19.977786319786077 -26.597348502634084
VL30-all 0.18333159143858438 0.11592515539476238
RT-VL30-all -20.813347353977118 0.768526339355562 -20.000630053325267 -21.74582889837823
*** budget = 40
VL40-1 0.2939943433925021 0.05472923847013149
RT-VL40-1 -22.359219760074705 7.218700456465672 -20.000024138165212 -56.528423651861445
VL40-2 0.13588097617449332 0.04986114638247923
RT-VL40-2 -22.359219760074705 7.218700456465672 -20.000024138165212 -56.528423651861445
VL40-3 0.0008856734702339206 8.779459961771572e-05
RT-VL40-3 -20.000842942435558 0.004912825622026245 -20.000024138165212 -20.030319896167708
VL40-4 0.1553001191961268 0.025577155239862818
RT-VL40-4 -20.388132681052674 1.381729252374744 -19.977786319786077 -26.597348502634084
VL40-5 0.31505223595314497 0.027039268082281746
RT-VL40-5 -20.388132681052674 1.381729252374744 -19.977786319786077 -26.597348502634084
VL40-all 0.18022266963730021 0.11477986164466969
RT-VL40-all -21.09910956493806 1.0385491072372481 -20.000842942435558 -22.359219760074705
*** budget = 50
VL50-1 0.2914498399231014 0.0581176284613569
RT-VL50-1 -21.69208542139705 4.877756236557176 -20.000024138165212 -41.37193283021695
VL50-2 0.1337766870208907 0.05254062393516022
RT-VL50-2 -21.69208542139705 4.877756236557176 -20.000024138165212 -41.37193283021695
VL50-3 0.0008822592358043428 9.71842797797674e-05
RT-VL50-3 -20.001033996765297 0.0054382549933500186 -20.000024138165212 -20.030319896167708
VL50-4 0.15427615918309254 0.02827079002441656
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VL50-5 0.3139704365167483 0.029891061201194537
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VL50-all 0.17887107637592745 0.1142315613499491
RT-VL50-all -20.868814008580834 0.6945256722856826 -20.001033996765297 -21.69208542139705
logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00009_9_clip_discriminator=10,dr_cc=0.9000,seed=5_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s5/08.10_20.43.23/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00009_9_clip_discriminator=10,dr_cc=0.9000,seed=5_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s5/08.10_20.43.23/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/ablo/run_2024-08-10_20-42-43/run_9f213_00009_9_clip_discriminator=10,dr_cc=0.9000,seed=5_2024-08-10_20-42-44/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg00_ds0.001_zt=p_g0_s5/08.10_20.43.23/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 -8.486306096883128 3.5520315980528276 -4.513916015924693 -36.93351123343834
LR -0.03961752652608838 0.05225520049596934 -7.8330460958842e-05 -0.6112635552377691
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.06327026908876876 0.07217877110669128
RT-VL10-1 -6.5583961307047955 1.4161789985745687 -4.680094749401093 -14.21183694202391
VL10-2 0.046124073692915414 0.04261105661364804
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VL10-3 0.03735084808066846 0.043068261395087945
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VL10-4 0.038714762271672955 0.0354873401387123
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VL10-5 0.034081154997626636 0.03351831337296082
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VL10-all 0.04390822162633044 0.010453145830127063
RT-VL10-all -7.7969102499667855 0.8862844707528211 -6.5583961307047955 -8.69186913012596
*** budget = 20
VL20-1 0.02864810468624178 0.030841599487745147
RT-VL20-1 -6.407287102726969 1.2681884423975773 -4.680094749401093 -12.07976750108551
VL20-2 0.022864370484109806 0.02159442991486695
RT-VL20-2 -6.423929721746645 0.7364563283941951 -5.1473576946454145 -8.344730549516814
VL20-3 0.01582007919042206 0.014439006491405873
RT-VL20-3 -8.303040937708557 3.5577793407662486 -5.919678682207144 -20.000024138165212
VL20-4 0.0175948623351253 0.016378177101821403
RT-VL20-4 -8.102963454340259 3.04729328512298 -6.167612949226642 -27.801175328305956
VL20-5 0.01855901547722445 0.01842443695144177
RT-VL20-5 -8.626128684410036 0.8737109639306451 -4.8006426989730535 -10.419213342745058
VL20-all 0.020697286434624682 0.004602089799886543
RT-VL20-all -7.572669980186494 0.9593903875130708 -6.407287102726969 -8.626128684410036
*** budget = 30
VL30-1 0.018123665016106152 0.0216197667528805
RT-VL30-1 -6.412938795043978 1.2082768453032358 -5.2298515949121 -12.07976750108551
VL30-2 0.017910053876976036 0.017202916042867323
RT-VL30-2 -6.37705476162748 0.7218170536468268 -5.1473576946454145 -8.344730549516814
VL30-3 0.010739867466254414 0.011232016146239155
RT-VL30-3 -9.128194194560205 4.80095128460493 -5.935440208886719 -25.203475272017354
VL30-4 0.011223902131631283 0.009804380701297686
RT-VL30-4 -8.109661193029087 3.2178192412563495 -6.167612949226642 -27.801175328305956
VL30-5 0.011760234506496086 0.008944934554487182
RT-VL30-5 -8.663813813797208 0.893791250106773 -4.8006426989730535 -10.240601000336502
VL30-all 0.013951544599492794 0.0033356596899252206
RT-VL30-all -7.738332551611592 1.1433163818109056 -6.37705476162748 -9.128194194560205
*** budget = 40
VL40-1 0.015610518298681052 0.019862844176268945
RT-VL40-1 -6.35587172520477 1.1725389726722684 -5.2298515949121 -10.886140997384818
VL40-2 0.010917843274966074 0.011238303936795662
RT-VL40-2 -6.342574767957334 0.5870100211139204 -5.1473576946454145 -7.617804427970147
VL40-3 0.007148554652415805 0.007889590332769689
RT-VL40-3 -9.343465018609612 4.733314610897451 -5.935440208886719 -20.000024138165212
VL40-4 0.00914521115402448 0.009251547397952814
RT-VL40-4 -8.376628873688288 3.682786386786041 -6.139557204274272 -27.801175328305956
VL40-5 0.009421965205559326 0.009125470848816288
RT-VL40-5 -8.719805079336691 0.9759293171477073 -4.8006426989730535 -10.240601000336502
VL40-all 0.010448818517129348 0.002846355224341903
RT-VL40-all -7.82766909295934 1.2463220605564096 -6.342574767957334 -9.343465018609612
*** budget = 50
VL50-1 0.010402256598238624 0.009521559917067825
RT-VL50-1 -6.236699586158168 0.9146550844813709 -5.340504199512245 -8.646136584723012
VL50-2 0.013324846107855236 0.016886399782915933
RT-VL50-2 -6.405676174321659 0.5407865711308156 -5.284908176385077 -7.617804427970147
VL50-3 0.005219417259840353 0.005419393701332813
RT-VL50-3 -10.04011000898648 5.029525290669702 -5.935440208886719 -20.000024138165212
VL50-4 0.006347936064355153 0.006960520729366405
RT-VL50-4 -8.036294039721211 1.8813859141440525 -6.167612949226642 -17.05130140649209
VL50-5 0.007997446145348844 0.0068924077418669304
RT-VL50-5 -8.60982687084607 0.9361424888984278 -4.8006426989730535 -10.240601000336502
VL50-all 0.00865838043512764 0.0029126778398523034
RT-VL50-all -7.865721336006717 1.420999502537091 -6.236699586158168 -10.04011000898648
logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00009_9_dr_cc=0.9000,seed=5_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s5/05.30_19.34.56/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00009_9_dr_cc=0.9000,seed=5_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s5/05.30_19.34.56/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 -10.056178348684437 3.0426296565378554 -6.266268753432023 -34.605415973277644
LR -0.03146971895419515 0.049409180463114634 -2.058292205564527e-06 -0.4035239550252975
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.12785562824006094 0.10322934170979943
RT-VL10-1 -10.11087024041426 3.152376260867143 -8.10435190745023 -34.605415973277644
VL10-2 0.07275702070428018 0.04327101328521595
RT-VL10-2 -10.21116872244812 3.1773360859078434 -8.054882515923387 -34.605415973277644
VL10-3 0.018789509368677095 0.01986016152902826
RT-VL10-3 -12.949185786949382 5.116690978126952 -7.4974732587076485 -22.111656660363582
VL10-4 0.03463757285771299 0.03285512899195953
RT-VL10-4 -9.369263709837632 1.5073078500419244 -6.550221172727449 -20.76683639643925
VL10-5 0.029944525714146693 0.03212117092816608
RT-VL10-5 -8.818546134664517 1.353179338451855 -7.02446147606602 -14.874109842049716
VL10-all 0.05679685137697558 0.03990166873394303
RT-VL10-all -10.291806918862783 1.4228482954733523 -8.818546134664517 -12.949185786949382
*** budget = 20
VL20-1 0.06907983095972993 0.06757961733908276
RT-VL20-1 -9.76634606387478 2.9456500076561536 -8.159141457612137 -34.605415973277644
VL20-2 0.048659358673354684 0.033505990790664346
RT-VL20-2 -10.10159554321752 3.2726733865200925 -8.054882515923387 -34.605415973277644
VL20-3 0.008458091757994796 0.008110370571918189
RT-VL20-3 -13.06669520326186 5.084025108989374 -7.4974732587076485 -21.45964951105583
VL20-4 0.01776264042775094 0.015623923521348506
RT-VL20-4 -9.417896325797383 1.4712650070214286 -8.362546982894862 -20.76683639643925
VL20-5 0.01594381113611378 0.016626138398346285
RT-VL20-5 -8.73055009257083 1.4010984860357174 -7.02446147606602 -14.874109842049716
VL20-all 0.03198074659098883 0.023096195764835903
RT-VL20-all -10.216616645744473 1.4956783868801442 -8.73055009257083 -13.06669520326186
*** budget = 30
VL30-1 0.04914741019672896 0.05438929862894597
RT-VL30-1 -9.351298392841862 0.5652434244521933 -8.159141457612137 -11.086245152228853
VL30-2 0.03717962310636908 0.02949797188771865
RT-VL30-2 -9.702037574859176 1.2282242100097274 -8.131175170209929 -15.874886060743112
VL30-3 0.005674075981048929 0.0061300180291743965
RT-VL30-3 -13.114562287011115 5.260953274370835 -7.4974732587076485 -22.111656660363582
VL30-4 0.012218082853663262 0.011508714282231058
RT-VL30-4 -9.602332415077864 1.8551184872119768 -8.362546982894862 -20.76683639643925
VL30-5 0.010483931914119565 0.011193725877017377
RT-VL30-5 -8.857318878950384 1.6523557289837607 -7.056438113357109 -14.874109842049716
VL30-all 0.022940624810385956 0.017075248081127863
RT-VL30-all -10.12550990974808 1.5228387693300571 -8.857318878950384 -13.114562287011115
*** budget = 40
VL40-1 0.03801301981373469 0.052198041025267226
RT-VL40-1 -9.380422105768503 0.5959995566056435 -8.237133501745864 -11.086245152228853
VL40-2 0.030680251802285147 0.022397013459364724
RT-VL40-2 -10.31692199088835 2.7200074243586725 -9.005660271055117 -24.51652506102969
VL40-3 0.003481976997779616 0.003304321447942224
RT-VL40-3 -13.000303807434884 4.934377401538253 -8.800323169774185 -21.45964951105583
VL40-4 0.009627203451056568 0.008176018165443036
RT-VL40-4 -9.521619938239345 1.9834545473430953 -8.362546982894862 -20.76683639643925
VL40-5 0.00801694180991801 0.0061903290830850775
RT-VL40-5 -8.81686998126732 1.5765084902332562 -7.056438113357109 -14.874109842049716
VL40-all 0.017963878774954808 0.013724732339024768
RT-VL40-all -10.207227564719682 1.4764868478286899 -8.81686998126732 -13.000303807434884
*** budget = 50
VL50-1 0.02796775130799805 0.04077163947170948
RT-VL50-1 -9.352926500538956 0.6274796696572299 -8.237133501745864 -11.086245152228853
VL50-2 0.024403227777646603 0.018219640823656458
RT-VL50-2 -9.964178827113246 1.4783087985560892 -9.005660271055117 -15.874886060743112
VL50-3 0.0024803085696120885 0.001962597152290801
RT-VL50-3 -12.573173999708548 4.822979941474687 -9.151546764808092 -21.45964951105583
VL50-4 0.007276378208980387 0.005478949473446424
RT-VL50-4 -9.69182884361064 2.18931410788553 -8.362546982894862 -20.76683639643925
VL50-5 0.0062686978492215795 0.005927591963492436
RT-VL50-5 -8.600290190748806 1.2777605169495319 -7.610945274927632 -13.471697271199469
VL50-all 0.013679272742691742 0.010397060243303722
RT-VL50-all -10.03647967234404 1.3482369882317096 -8.600290190748806 -12.573173999708548
logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00007_7_dr_cc=0.9000,seed=4_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s4/05.30_19.34.56/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00007_7_dr_cc=0.9000,seed=4_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s4/05.30_19.34.56/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 -14.510199360595614 5.894105322331562 -6.393904691843033 -37.82855394196107
LR -0.10669524639622827 0.09683488208964805 -1.5373371085258825e-05 -0.3966443509547155
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.029805873598177744 0.0325433946287678
RT-VL10-1 -8.75454741866978 1.9887496906570452 -7.0192508253201344 -18.49913715575616
VL10-2 0.0857956192704404 0.052089113330196994
RT-VL10-2 -12.341844719253348 6.125881168445265 -7.856287997121954 -37.82855394196107
VL10-3 0.0008993808032507562 1.1292287685732575e-05
RT-VL10-3 -19.92333373882495 0.9361256247397076 -8.496464237126252 -20.000024138165212
VL10-4 0.12691561643770066 0.04210342385069854
RT-VL10-4 -14.263012144230572 6.051982977039444 -7.115608101625907 -31.26414168591168
VL10-5 0.15162850483671808 0.13756690770742358
RT-VL10-5 -12.797597794340884 6.033881830173294 -6.393904691843033 -31.26414168591168
VL10-all 0.07900899898925753 0.05680977880811696
RT-VL10-all -13.616067163063908 3.6367034147796966 -8.75454741866978 -19.92333373882495
*** budget = 20
VL20-1 0.014004603666453199 0.012999797205902155
RT-VL20-1 -8.972304565926915 2.3589467464488534 -7.232267405080238 -18.49913715575616
VL20-2 0.05438480493292429 0.04189574066352848
RT-VL20-2 -11.151689120413403 5.857534119438576 -7.946957506327436 -37.82855394196107
VL20-3 0.0008984557029979727 1.5916026558136127e-05
RT-VL20-3 -19.846643339484686 1.319431519968611 -8.496464237126252 -20.000024138165212
VL20-4 0.10448648256807146 0.04428662480704651
RT-VL20-4 -11.672358301965415 5.599766136453022 -7.115608101625907 -31.26414168591168
VL20-5 0.0810588098225346 0.1075335679885853
RT-VL20-5 -10.507661030151286 5.09600246188215 -6.604959598301061 -31.26414168591168
VL20-all 0.0509666313385963 0.03912700165708768
RT-VL20-all -12.430131271588342 3.817479346800463 -8.972304565926915 -19.846643339484686
*** budget = 30
VL30-1 0.011026421716232084 0.009996476131479642
RT-VL30-1 -9.147011684690474 2.692907015412666 -7.232267405080238 -18.49913715575616
VL30-2 0.03514892901786061 0.030608427343655733
RT-VL30-2 -11.485274914911662 5.546927438002831 -7.946957506327436 -30.217223134110853
VL30-3 0.000897530602745189 1.9427105308456036e-05
RT-VL30-3 -19.769952940144435 1.6104983861454545 -8.496464237126252 -20.000024138165212
VL30-4 0.09243819503441657 0.04455124387495745
RT-VL30-4 -11.417228773898094 5.68416082187335 -7.115608101625907 -31.26414168591168
VL30-5 0.05094434984465055 0.08338936075485212
RT-VL30-5 -8.979039010958996 3.0931022530701138 -6.604959598301061 -20.000024138165212
VL30-all 0.038091085243181 0.03238310533425798
RT-VL30-all -12.159701464920733 3.95259301579006 -8.979039010958996 -19.769952940144435
*** budget = 40
VL40-1 0.00784662816339441 0.007158886318059652
RT-VL40-1 -9.181702517902954 2.516575996777259 -7.374181250097771 -18.49913715575616
VL40-2 0.027593441981432668 0.026191604774372292
RT-VL40-2 -10.456747824445634 4.897803751438245 -8.178247361047589 -29.097570591710177
VL40-3 0.000896555497073336 2.250243858122321e-05
RT-VL40-3 -19.689117113812813 1.865442146114426 -8.496464237126252 -20.000024138165212
VL40-4 0.0800025893383744 0.03868691803573657
RT-VL40-4 -9.914964243973039 4.762389161678571 -7.115608101625907 -31.26414168591168
VL40-5 0.027155108080611997 0.035876133523699845
RT-VL40-5 -8.2955271634844 0.8714496466036463 -7.115608101625907 -11.168940107532391
VL40-all 0.028698864612177362 0.027725300616045494
RT-VL40-all -11.507611772723768 4.154641073148645 -8.2955271634844 -19.689117113812813
*** budget = 50
VL50-1 0.0060414794817736225 0.005484237709570467
RT-VL50-1 -9.106454750887048 2.422796003974323 -7.374181250097771 -18.49913715575616
VL50-2 0.01754953730959345 0.015451647749504393
RT-VL50-2 -10.290544447693962 4.580270911505575 -8.08007492478151 -29.097570591710177
VL50-3 0.0008956804022396218 2.4909086621807944e-05
RT-VL50-3 -19.616572141463916 2.0649521978612895 -8.496464237126252 -20.000024138165212
VL50-4 0.06977593073814967 0.03407002610226331
RT-VL50-4 -10.423547999511417 5.291039083004881 -7.527194090994916 -31.26414168591168
VL50-5 0.027158881316601637 0.03970772819962602
RT-VL50-5 -8.25822831968753 0.8737975279065799 -6.866075378717204 -11.168940107532391
VL50-all 0.024284301849671598 0.024506883688494366
RT-VL50-all -11.539069531848776 4.11657103218639 -8.25822831968753 -19.616572141463916
logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00005_5_dr_cc=0.9000,seed=3_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s3/05.30_19.34.56/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00005_5_dr_cc=0.9000,seed=3_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s3/05.30_19.34.56/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 -14.025985889487218 5.758725909394024 -5.213716768700926 -35.06807221695496
LR -0.0980724154401718 0.09178638627238676 -5.8076777959659154e-05 -0.4168921634433288
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.087393659729926 0.08617433787145232
RT-VL10-1 -10.041545377463757 5.2316059512139494 -5.51732255029171 -27.575125447591972
VL10-2 0.08568455991277389 0.054623228775351605
RT-VL10-2 -13.702327003680523 6.361286292891163 -7.215344613451805 -35.06807221695496
VL10-3 0.0009003059035035103 4.9095909949331226e-17
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.05591753321099626 0.0470750044265846
RT-VL10-4 -11.189496842371184 3.7469917629607914 -6.940170789885815 -20.000024138165212
VL10-5 0.04893147390158137 0.04667195530101047
RT-VL10-5 -9.82870983060847 2.805342967795477 -6.394355901916226 -20.000024138165212
VL10-all 0.0557655065317562 0.03147191504825532
RT-VL10-all -12.952420638457829 3.7833778281488084 -9.82870983060847 -20.00002413816521
*** budget = 20
VL20-1 0.03990139964049353 0.04291000464807797
RT-VL20-1 -8.630149105360982 3.4109129084744207 -5.680153793457439 -27.13599808332938
VL20-2 0.0549829963555678 0.04531802895852356
RT-VL20-2 -13.122531031636342 6.568440955430315 -7.424302546208257 -35.06807221695496
VL20-3 0.000900305903503491 5.511004228684604e-17
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.03071045747278537 0.027609878870049613
RT-VL20-4 -10.644805216917895 3.145921906829783 -7.75018370873657 -19.991738315469
VL20-5 0.027405180659192264 0.02406694862310935
RT-VL20-5 -9.828184077155539 2.8327982669725236 -6.498086178404313 -18.614318892028347
VL20-all 0.030780068006308486 0.01773795549105076
RT-VL20-all -12.445138713847193 4.054107845390845 -8.630149105360982 -20.00002413816521
*** budget = 30
VL30-1 0.028800657681238646 0.036285219805970284
RT-VL30-1 -8.259901054146205 2.3997602173649097 -5.680153793457439 -16.5574000544684
VL30-2 0.03886001876985015 0.03481369047321433
RT-VL30-2 -13.197004762683257 6.897673208658919 -7.424302546208257 -35.06807221695496
VL30-3 0.0009003059035034733 5.438961551040562e-17
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.023816952607793086 0.025158721763445552
RT-VL30-4 -10.695010483760434 3.1549046448688793 -7.776197644792559 -19.77229778061947
VL30-5 0.020369848607347505 0.020108285067938162
RT-VL30-5 -9.722993955523917 2.8793592755292265 -6.664281897595844 -18.614318892028347
VL30-all 0.02254955671394657 0.012491236257597037
RT-VL30-all -12.374986878855806 4.137782621293556 -8.259901054146205 -20.000024138165216
*** budget = 40
VL40-1 0.023349126724370978 0.03015230814087134
RT-VL40-1 -8.261300045482772 2.3062249795906093 -5.736460813405041 -14.988468741915248
VL40-2 0.03028332711327725 0.026343895636233305
RT-VL40-2 -12.687051976333933 6.326844057532203 -7.424302546208257 -34.96881477613636
VL40-3 0.0009003059035034618 5.074481898781983e-17
RT-VL40-3 -20.00002413816522 7.105427357601002e-15 -20.000024138165212 -20.000024138165212
VL40-4 0.015457599480240725 0.012483914797001136
RT-VL40-4 -10.53832301463726 3.1523885580850446 -7.776197644792559 -19.77229778061947
VL40-5 0.01354520829709549 0.011673312541578489
RT-VL40-5 -10.050260538157692 3.186121322600607 -6.664281897595844 -18.614318892028347
VL40-all 0.01670711350369758 0.009897919093793844
RT-VL40-all -12.307391942555373 4.096726462095854 -8.261300045482772 -20.00002413816522
*** budget = 50
VL50-1 0.01859837498436044 0.02117855053794646
RT-VL50-1 -8.372550454487412 2.5779487349393904 -5.736460813405041 -16.5574000544684
VL50-2 0.026350089836362 0.024825529599066025
RT-VL50-2 -12.690834323138347 6.232408863264154 -7.424302546208257 -29.04122833144506
VL50-3 0.0009003059035034585 4.9095909949331226e-17
RT-VL50-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL50-4 0.014976125965900288 0.01343345139372328
RT-VL50-4 -10.868361996258972 3.55921193876155 -7.776197644792559 -19.77229778061947
VL50-5 0.010582817779653782 0.010542680111817032
RT-VL50-5 -9.537394270104796 2.8761498201204008 -6.664281897595844 -17.640924411692858
VL50-all 0.014281542893955996 0.008455830900715314
RT-VL50-all -12.293833036430948 4.112161737199161 -8.372550454487412 -20.000024138165216
logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00003_3_dr_cc=0.9000,seed=2_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s2/05.30_19.34.54/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00003_3_dr_cc=0.9000,seed=2_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s2/05.30_19.34.54/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 -12.442343252697242 5.840957967376941 -5.653310911886071 -31.21208766495302
LR -0.08117017343511969 0.08903488662221552 -0.00021594279971659525 -0.5004412606710796
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0526215304313848 0.06933703674823338
RT-VL10-1 -9.04999941353622 3.4728632488247495 -6.293805160127734 -23.231307974605222
VL10-2 0.057853967151578124 0.04569117433837271
RT-VL10-2 -9.227407425176352 3.034720268082781 -6.657815734354134 -20.000024138165212
VL10-3 0.0014657820680154302 0.005101825193021227
RT-VL10-3 -19.58199112322037 2.1943761977980634 -7.451131007899929 -20.000024138165212
VL10-4 0.07553076930005696 0.05358872821562688
RT-VL10-4 -10.369018647128193 4.279502104160955 -6.95242779014514 -20.231764197281844
VL10-5 0.04803943980226876 0.05704444770122916
RT-VL10-5 -8.217045092129219 2.1640392634578745 -5.824087220437583 -20.081779420368548
VL10-all 0.04710229775066081 0.024648765152853597
RT-VL10-all -11.28909234023807 4.202862404208698 -8.217045092129219 -19.58199112322037
*** budget = 20
VL20-1 0.021113249698888416 0.022196421208142962
RT-VL20-1 -8.152954279906533 1.8291783934801942 -6.293805160127734 -15.808207197979643
VL20-2 0.030528544514579262 0.023718084828750118
RT-VL20-2 -8.474758032842606 1.3516407997958175 -6.763702914273378 -17.681678283710603
VL20-3 0.0008601218752808437 0.0001678218453962326
RT-VL20-3 -19.496810914142277 2.402822892414935 -7.4783261164706705 -20.000024138165212
VL20-4 0.045001339468580544 0.03895196181535325
RT-VL20-4 -8.947362100909485 1.9491582005181414 -7.286467823916638 -20.000024138165212
VL20-5 0.021733552470694112 0.02004594308751557
RT-VL20-5 -8.05401077893093 1.7612149171461244 -6.90890839041726 -19.539081494790064
VL20-all 0.023847361605604637 0.014370008572628651
RT-VL20-all -10.625179221346366 4.446752383269572 -8.05401077893093 -19.496810914142277
*** budget = 30
VL30-1 0.012419269640451687 0.012343388092709223
RT-VL30-1 -8.037016345209532 2.05201502324325 -6.293805160127734 -19.5154904665125
VL30-2 0.021798802669814113 0.016247890784186893
RT-VL30-2 -8.506083547737415 1.4405863759926785 -7.337728291965552 -17.681678283710603
VL30-3 0.0008400298611694956 0.00020257144565595536
RT-VL30-3 -19.245204302130823 2.9103984464066515 -7.4783261164706705 -20.000024138165212
VL30-4 0.03489220832786041 0.03193676188943798
RT-VL30-4 -8.754419921769035 1.5642437054234415 -7.286467823916638 -14.657251354581433
VL30-5 0.015433802149561093 0.014935809542570068
RT-VL30-5 -7.883041925144324 1.1702431104810995 -6.832693063834768 -12.726991197800553
VL30-all 0.01707682252977136 0.011204559952336109
RT-VL30-all -10.485153208398227 4.3912414378474045 -7.883041925144324 -19.245204302130823
*** budget = 40
VL40-1 0.010869151256599659 0.013858392913340366
RT-VL40-1 -8.271740345312276 2.124708208058882 -6.293805160127734 -15.564831825789186
VL40-2 0.017655324657785954 0.015171477196649864
RT-VL40-2 -8.923597883048084 2.8576385159351627 -7.337728291965552 -22.98496796355735
VL40-3 0.0008188517922413179 0.0002317928374236634
RT-VL40-3 -18.97999733271334 3.343051615255739 -7.4783261164706705 -20.000024138165212
VL40-4 0.02568333133020384 0.025945284711352108
RT-VL40-4 -8.872281665868922 1.4624206093273566 -7.446525729776556 -14.657251354581433
VL40-5 0.011406141915975299 0.011690182801206983
RT-VL40-5 -7.798543283381017 0.8953280788185777 -7.05372083832688 -12.10478852092638
VL40-all 0.013286560190561214 0.008216929222108368
RT-VL40-all -10.569232102064728 4.2257285628188574 -7.798543283381017 -18.97999733271334
*** budget = 50
VL50-1 0.008176719692655167 0.008978038575917382
RT-VL50-1 -7.811106523758139 1.2982020675596226 -6.293805160127734 -12.501996402211255
VL50-2 0.014690109304034174 0.011671292720681618
RT-VL50-2 -8.309233955759128 0.6362709442042317 -7.337728291965552 -9.221848700663942
VL50-3 0.0007998458329467994 0.0002536830581941438
RT-VL50-3 -18.741991078107894 3.672098354165486 -7.4783261164706705 -20.000024138165212
VL50-4 0.017919860234571806 0.019805981253101512
RT-VL50-4 -9.091638573953864 1.6696517538665803 -7.446525729776556 -14.657251354581433
VL50-5 0.00877572887327216 0.009795703693577539
RT-VL50-5 -7.716656094561783 0.6111647951237906 -7.182677183917931 -10.2857396366967
VL50-all 0.01007245278749602 0.005901882614137583
RT-VL50-all -10.334125245228162 4.232090120726322 -7.716656094561783 -18.741991078107894
logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00001_1_dr_cc=0.9000,seed=1_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s1/05.30_19.34.56/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/ig/run_2024-05-30_19-34-23/run_258a1_00001_1_dr_cc=0.9000,seed=1_2024-05-30_19-34-23/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.90_reg00_ds0.001_s1/05.30_19.34.56/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 -9.701468990326456 3.703320521791831 -5.418697406440314 -31.68962746840453
LR -0.034363308881592225 0.057085374188983955 -8.45768995727525e-06 -0.41899241372261464
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0285136173917684 0.022518302522882682
RT-VL10-1 -8.625193276122673 1.5908937702200716 -7.06746625764703 -18.72501788425853
VL10-2 0.05078723715701596 0.04414892500306505
RT-VL10-2 -9.784776862971466 3.1378835300835397 -7.320882193175531 -23.687049317606107
VL10-3 0.03110687994536723 0.04925023381783867
RT-VL10-3 -13.746973580328007 5.794815656898046 -7.063573727789435 -22.938230941465797
VL10-4 0.05987740920393383 0.04496512803508327
RT-VL10-4 -9.350191605225897 3.6778316971045015 -7.1224278208520815 -28.15657631504486
VL10-5 0.04358158414162007 0.054399692818124526
RT-VL10-5 -8.528920937061274 2.0888972986362213 -6.643907490909683 -20.000024138165212
VL10-all 0.0427733455679411 0.011805684805537947
RT-VL10-all -10.007211252341863 1.9267739922959661 -8.528920937061274 -13.746973580328007
*** budget = 20
VL20-1 0.014933917538266327 0.012091196399711948
RT-VL20-1 -8.66025394716471 1.7903979151493405 -7.06746625764703 -18.72501788425853
VL20-2 0.024990887465026923 0.025608075760154184
RT-VL20-2 -9.32630956262833 1.9637633467522102 -7.320882193175531 -19.505470737223845
VL20-3 0.008680850119336144 0.015958736177075307
RT-VL20-3 -16.41757411140359 5.340201361106536 -7.65814563071518 -20.37619514541232
VL20-4 0.03523111858377933 0.029434644828693162
RT-VL20-4 -9.062601829903878 2.9909436437258905 -7.1224278208520815 -26.37187596060345
VL20-5 0.022089826974551682 0.03150263039546361
RT-VL20-5 -8.385891417902705 1.8007336725605847 -6.7491608059447925 -14.500727863987896
VL20-all 0.02118532013619208 0.00903356778010749
RT-VL20-all -10.370526173800641 3.040777840737278 -8.385891417902705 -16.41757411140359
*** budget = 30
VL30-1 0.01144886491235803 0.01127443092315203
RT-VL30-1 -8.74202635111538 1.9312153638754532 -7.06746625764703 -18.72501788425853
VL30-2 0.015273619145453778 0.016513518278406192
RT-VL30-2 -9.317118868672171 1.67545964915733 -7.951924650035371 -19.149073813183
VL30-3 0.005117384969471554 0.01164573962557298
RT-VL30-3 -17.161595943006088 5.052288200423674 -7.4864541106339715 -20.000024138165212
VL30-4 0.027154589404317555 0.025240389755119556
RT-VL30-4 -9.004274601473831 2.577356747796123 -7.39566690428411 -24.625473761535307
VL30-5 0.012930498675326425 0.01462812404288572
RT-VL30-5 -8.407685367256795 1.7993126035637887 -6.7491608059447925 -14.500727863987896
VL30-all 0.014384991421385468 0.007217717218769304
RT-VL30-all -10.526540226304855 3.3310056349704755 -8.407685367256795 -17.161595943006088
*** budget = 40
VL40-1 0.00794550877500536 0.006965001264218269
RT-VL40-1 -8.4954092092658 1.3980086774172318 -7.246680259508894 -16.014559570519346
VL40-2 0.009110642007190226 0.008481449251493687
RT-VL40-2 -9.431156679395679 1.8647119615561791 -7.862008637352659 -19.149073813183
VL40-3 0.0012614470257676354 0.0023968269780495852
RT-VL40-3 -18.77340639612573 3.535059208294158 -7.909740892641804 -20.000024138165212
VL40-4 0.019642852948852506 0.019846874641942163
RT-VL40-4 -9.181361192277139 2.9021480535068607 -7.39566690428411 -24.625473761535307
VL40-5 0.009178300776169393 0.00845454899805169
RT-VL40-5 -8.353581502629401 1.6532262547451075 -6.87500709988298 -13.128422687882727
VL40-all 0.009427750306597024 0.005888915926824872
RT-VL40-all -10.846982995938749 3.9838172235052465 -8.353581502629401 -18.77340639612573
*** budget = 50
VL50-1 0.006527492342790042 0.005739243420911364
RT-VL50-1 -8.583888879536547 1.5192739484904378 -7.246680259508894 -16.014559570519346
VL50-2 0.0070388852597048635 0.007436967944330205
RT-VL50-2 -9.421446097274945 2.0240231320272364 -8.052843505422858 -19.149073813183
VL50-3 0.0013457132876292578 0.0026547495761506273
RT-VL50-3 -18.487195589649843 3.8703409714540107 -7.909740892641804 -20.000024138165212
VL50-4 0.01762125893173798 0.012847053410672384
RT-VL50-4 -9.124332990623888 3.0596286178877885 -7.39566690428411 -24.625473761535307
VL50-5 0.007538155264105669 0.007704774912874567
RT-VL50-5 -8.281466334863486 1.6691197889382539 -6.87500709988298 -13.128422687882727
VL50-all 0.008014301017193562 0.005294382936493085
RT-VL50-all -10.779665978389742 3.8743603286492467 -8.281466334863486 -18.487195589649843
logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00034_34_dl_scale=1.0000,dr_cc=0.8000,seed=5_2024-05-30_19-34-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s5/05.31_08.21.47/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00034_34_dl_scale=1.0000,dr_cc=0.8000,seed=5_2024-05-30_19-34-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s5/05.31_08.21.47/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 -10.289364593866473 5.927182159120072 -4.12125159553605 -44.805104874860135
LR -0.06968719994901511 0.07980457286025633 -2.1071255185578508e-05 -0.601963456818035
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.048523433639006935 0.04910921665538242
RT-VL10-1 -7.8207666072498645 2.8121390358411658 -4.646060747296546 -35.245799098570984
VL10-2 0.029391475837555414 0.029281279629099464
RT-VL10-2 -8.531711124475116 3.441076103656286 -4.215512006785791 -27.498964561094095
VL10-3 0.006664194393077207 0.015253251965853327
RT-VL10-3 -18.08122156714236 5.154910962726953 -4.159151457636561 -29.396247391770377
VL10-4 0.07443509284224117 0.04837193891527747
RT-VL10-4 -10.156932553036055 6.099679990752154 -4.270599020870304 -32.35677923792055
VL10-5 0.04584416413472055 0.06383628292978025
RT-VL10-5 -7.972180222180432 3.1099280975981607 -5.654302527642333 -28.701153641583968
VL10-all 0.04097167216932025 0.022412830168590653
RT-VL10-all -10.512562414816767 3.8738650506030505 -7.8207666072498645 -18.08122156714236
*** budget = 20
VL20-1 0.02050296833694789 0.018317213770227936
RT-VL20-1 -7.74129945061252 3.4284612614426933 -4.646060747296546 -35.245799098570984
VL20-2 0.01639824714281236 0.018486992417360466
RT-VL20-2 -8.581423271049804 3.3144498811911793 -4.215512006785791 -22.516727699545935
VL20-3 0.0019372862332599878 0.005613895151023517
RT-VL20-3 -19.68147999474893 2.6805023765667753 -7.2955389168443 -26.2059780998149
VL20-4 0.049198997512872926 0.034293847003977876
RT-VL20-4 -9.528511421207163 5.615512040489817 -4.5522506068170685 -30.29707954632606
VL20-5 0.021335087212655292 0.028024825044802585
RT-VL20-5 -7.4879115987737634 1.6514545164961063 -5.654302527642333 -13.664964168530496
VL20-all 0.02187451728770969 0.015338070724337875
RT-VL20-all -10.604125147278436 4.59463157704908 -7.4879115987737634 -19.68147999474893
*** budget = 30
VL30-1 0.01544192940941902 0.014338989999199372
RT-VL30-1 -8.004920203465318 4.043973115629121 -6.091981863498612 -35.245799098570984
VL30-2 0.009382185689187954 0.009070909676007799
RT-VL30-2 -8.413015459921775 3.2781485357450486 -4.805585804994426 -22.516727699545935
VL30-3 0.0008513651860856086 0.00017047281297930076
RT-VL30-3 -19.968225665670907 1.8953519888006107 -8.040409172309472 -24.055908555555106
VL30-4 0.035971995908927454 0.02833804187057
RT-VL30-4 -9.716613199665417 5.660426502041757 -4.667736722161891 -29.87397928139162
VL30-5 0.01091868731991929 0.010879369637852292
RT-VL30-5 -7.149228304070358 1.1446260578698972 -5.654302527642333 -10.602367699918311
VL30-all 0.014513232702707865 0.011723787953722776
RT-VL30-all -10.650400566558755 4.731944401675617 -7.149228304070358 -19.968225665670907
*** budget = 40
VL40-1 0.01020899419517074 0.011048466643208391
RT-VL40-1 -8.111746359928544 4.706032537454085 -4.646060747296546 -35.245799098570984
VL40-2 0.007686671477125714 0.009055037412876568
RT-VL40-2 -8.771888744364833 3.7473802580821043 -4.913087773409414 -22.516727699545935
VL40-3 0.0008341697988847139 0.00019528020539860135
RT-VL40-3 -19.957053229389125 2.2031929823837886 -8.040409172309472 -24.055908555555106
VL40-4 0.028718021182357566 0.022434296576225362
RT-VL40-4 -8.894984126949138 4.330067472751789 -4.667736722161891 -27.397412286475145
VL40-5 0.007432452970415018 0.006356800211140021
RT-VL40-5 -7.299723547332273 1.394791248133224 -6.124094133591194 -11.769721149276375
VL40-all 0.01097606192479075 0.009397879984669117
RT-VL40-all -10.607079201592782 4.709277976374782 -7.299723547332273 -19.957053229389125
*** budget = 50
VL50-1 0.00702677164412342 0.006800958822607706
RT-VL50-1 -7.884838101437905 2.003260131530257 -5.073091605890863 -13.666102238519075
VL50-2 0.005910563753108059 0.004685070687297092
RT-VL50-2 -8.195402499276154 3.1628869118078713 -4.805585804994426 -22.516727699545935
VL50-3 0.0008187380411403211 0.00021394775095863333
RT-VL50-3 -19.94702668400804 2.4466593095535787 -8.040409172309472 -24.055908555555106
VL50-4 0.025203550184536214 0.021096501330530024
RT-VL50-4 -8.221627434048587 4.146489383474307 -4.667736722161891 -29.87397928139162
VL50-5 0.006555752262916884 0.005876268423370365
RT-VL50-5 -7.249788522175136 1.2232608063644137 -6.124094133591194 -10.602367699918311
VL50-all 0.00910307517716498 0.008352814577491964
RT-VL50-all -10.299736648189164 4.836339188143557 -7.249788522175136 -19.94702668400804
logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00026_26_dl_scale=1.0000,dr_cc=0.8000,seed=4_2024-05-30_19-34-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s4/05.31_08.21.16/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00026_26_dl_scale=1.0000,dr_cc=0.8000,seed=4_2024-05-30_19-34-10/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s4/05.31_08.21.16/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 -9.169231107918872 3.304343755027612 -5.4466537801551445 -25.1439214584092
LR -0.05164492010040316 0.04889790347020753 -7.892102837137038e-05 -0.43347150783785904
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.047486885520151165 0.04312596185600644
RT-VL10-1 -8.11459041452831 1.6004388315268283 -5.775696593406465 -15.340336296866212
VL10-2 0.034648965953418896 0.033719030534682795
RT-VL10-2 -8.859194177413299 1.9994423317882428 -6.047005183763349 -20.000024138165212
VL10-3 0.021701915487640486 0.03158453611171502
RT-VL10-3 -12.940960957631475 5.604678761147174 -5.942043747116045 -20.000024138165212
VL10-4 0.04163574985003442 0.03972157056897446
RT-VL10-4 -9.489700291818615 2.6199313046420603 -5.763011353792061 -20.000024138165212
VL10-5 0.06416905522059317 0.056569485080991314
RT-VL10-5 -7.5150885757229755 1.397096644379263 -5.5242548872464425 -14.834105322108858
VL10-all 0.04192851440636762 0.014055105732303138
RT-VL10-all -9.383906883422934 1.8996207790689785 -7.5150885757229755 -12.940960957631475
*** budget = 20
VL20-1 0.026020318130186702 0.02761000062698142
RT-VL20-1 -8.27468852082734 1.8387417163902826 -5.819385240122368 -15.340336296866212
VL20-2 0.017544405973965768 0.016778198736592472
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VL20-3 0.008076320298854507 0.01323009449363129
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VL20-4 0.018433633316937627 0.01861058564530593
RT-VL20-4 -9.097908006777214 1.582112513874749 -6.465152629095077 -14.495240293378579
VL20-5 0.035360029450420855 0.03630356904061828
RT-VL20-5 -7.251804305931462 1.3925124566336027 -5.5242548872464425 -14.834105322108858
VL20-all 0.02108694143407309 0.009131539967857977
RT-VL20-all -9.749787359806218 2.9118503849745347 -7.251804305931462 -15.442981475955428
*** budget = 30
VL30-1 0.01676904283242285 0.015970346646142452
RT-VL30-1 -8.34982203628981 1.9351837203329503 -5.819385240122368 -15.340336296866212
VL30-2 0.011636858732737995 0.010430817560366356
RT-VL30-2 -8.753576772268334 1.5036562089030396 -6.172439343616177 -11.13491209188183
VL30-3 0.003402349557453326 0.006326207342459233
RT-VL30-3 -17.40188691114996 4.914295424525276 -5.992750549666166 -20.000024138165212
VL30-4 0.011575668532057827 0.010559082453053571
RT-VL30-4 -9.016361640860467 1.5343737298687798 -6.465152629095077 -12.748221881992022
VL30-5 0.019561602090637054 0.016891691831830234
RT-VL30-5 -7.198147536703444 1.5444708623892165 -5.59358281003614 -14.834105322108858
VL30-all 0.012589104349061811 0.0055211936743985056
RT-VL30-all -10.143958979454402 3.681817179839888 -7.198147536703444 -17.40188691114996
*** budget = 40
VL40-1 0.012626113324964001 0.010288522120727695
RT-VL40-1 -8.21428860069931 2.1321385665848576 -5.819385240122368 -15.340336296866212
VL40-2 0.00867443446098371 0.006876070103695417
RT-VL40-2 -8.69901513459433 1.4806770876161708 -6.172439343616177 -10.87087447502936
VL40-3 0.002081003843344567 0.004225105270975891
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VL40-4 0.010216921062639849 0.010771532121543931
RT-VL40-4 -9.01680589615138 1.4610909574738742 -6.465152629095077 -12.477197514421977
VL40-5 0.013926515364562115 0.011667196080718955
RT-VL40-5 -7.021470778814079 1.6696218730441283 -5.59358281003614 -14.834105322108858
VL40-all 0.00950499761129885 0.0041377176354692085
RT-VL40-all -10.182098482736883 3.9471031093727555 -7.021470778814079 -17.95891200342532
*** budget = 50
VL50-1 0.010559057583946466 0.007986704475909304
RT-VL50-1 -8.332584633711834 2.302645170552195 -5.819385240122368 -15.340336296866212
VL50-2 0.008019284874020604 0.006894460641858155
RT-VL50-2 -8.705879282158657 1.4286116642674187 -6.172439343616177 -10.561131576660225
VL50-3 0.0014782055932189583 0.002580177614266126
RT-VL50-3 -18.725767473910413 3.7439450114525457 -5.992750549666166 -20.000024138165212
VL50-4 0.00688803757893733 0.008136443468463367
RT-VL50-4 -8.792732982902475 1.6114950772954546 -6.465152629095077 -12.477197514421977
VL50-5 0.010947701582860197 0.010873429658582045
RT-VL50-5 -7.0061269947316696 1.1703259943036894 -5.59358281003614 -9.744283205970403
VL50-all 0.007578457442596711 0.0034093947943170774
RT-VL50-all -10.312618273483011 4.255024294701535 -7.0061269947316696 -18.725767473910413
logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00018_18_dl_scale=1.0000,dr_cc=0.8000,seed=3_2024-05-30_19-34-07/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s3/05.30_19.34.41/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00018_18_dl_scale=1.0000,dr_cc=0.8000,seed=3_2024-05-30_19-34-07/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s3/05.30_19.34.41/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 -9.998636189902184 4.549015044706835 -3.908532747756241 -59.388722833587835
LR -0.0663390568747504 0.06098541231493157 -2.7505523171100776e-05 -0.6639288435745738
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.042417839663806946 0.0322280960481104
RT-VL10-1 -7.765944442739786 4.071993878893995 -4.302171153937788 -52.143230081095254
VL10-2 0.02922376007105555 0.027180609621853834
RT-VL10-2 -10.444276129687383 1.9297941158331648 -8.297868456386746 -30.17716018362236
VL10-3 0.010793974127537534 0.02087401082964819
RT-VL10-3 -16.167645385527837 5.301389020633473 -6.492821348860252 -20.634137168561775
VL10-4 0.05595854446181019 0.04457407953208783
RT-VL10-4 -9.001399629391779 2.9294998730011583 -5.626634507749878 -27.328434053231916
VL10-5 0.10708813354523403 0.08773902590725778
RT-VL10-5 -7.8155098442943824 2.8749805857245243 -4.982934329583587 -27.328434053231916
VL10-all 0.04909645037388885 0.032608989557508884
RT-VL10-all -10.238955086328234 3.121279782015737 -7.765944442739786 -16.167645385527837
*** budget = 20
VL20-1 0.02576820236505692 0.02312001780234055
RT-VL20-1 -7.994947609310822 5.324692404653238 -4.523033010932764 -52.143230081095254
VL20-2 0.014499129453087036 0.012684194538286729
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VL20-3 0.002532490403704791 0.005554988840394103
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VL20-4 0.03358680258424407 0.032181641645019785
RT-VL20-4 -8.475102329000855 1.4796522487403967 -6.069978122435804 -15.068537160566306
VL20-5 0.05252512869596266 0.05134959720908899
RT-VL20-5 -6.947769627184535 1.4299207083992846 -4.982934329583587 -13.72624628615529
VL20-all 0.025782350700411093 0.01699383964755605
RT-VL20-all -10.3937929996369 4.036933865006825 -6.947769627184535 -18.150933179214192
*** budget = 30
VL30-1 0.018741264258738698 0.014997653719270468
RT-VL30-1 -8.197218904936056 6.407231749957733 -4.746722737311779 -52.143230081095254
VL30-2 0.010627124058884347 0.00996109358642881
RT-VL30-2 -10.401593127056563 0.45461914282171934 -9.610101949790627 -11.461297722579062
VL30-3 0.001320098026808587 0.002798657197145505
RT-VL30-3 -18.826038900659015 3.5403739526939964 -6.866422138505387 -20.000024138165212
VL30-4 0.019024244274781398 0.015112400192453539
RT-VL30-4 -8.429763205932467 1.5276149971023787 -6.069978122435804 -15.068537160566306
VL30-5 0.033292593111600724 0.029650784465397314
RT-VL30-5 -6.861940309700028 1.4399933209486606 -4.982934329583587 -13.72624628615529
VL30-all 0.01660106474616275 0.010566506360845076
RT-VL30-all -10.543310889656826 4.292956743029117 -6.861940309700028 -18.826038900659015
*** budget = 40
VL40-1 0.015031891179118512 0.011535466565707624
RT-VL40-1 -7.189814061682535 1.1311801430602264 -5.4495583721204195 -10.154237874887174
VL40-2 0.00896201323315245 0.008824034279516793
RT-VL40-2 -10.426752447136312 0.5044680786691256 -9.610101949790627 -11.461297722579062
VL40-3 0.0013719530374300148 0.0032033873910708626
RT-VL40-3 -18.7685198172017 3.5527630839304547 -7.47960904492191 -20.000024138165212
VL40-4 0.015814449268564578 0.012853286011089022
RT-VL40-4 -8.463608591714642 1.7599302975176627 -6.017492829991177 -15.068537160566306
VL40-5 0.030049778838174702 0.029708678607726257
RT-VL40-5 -6.929910241226743 1.643893482571124 -4.982934329583587 -13.72624628615529
VL40-all 0.014246017111288051 0.009449853808018716
RT-VL40-all -10.355721031792386 4.384506012489736 -6.929910241226743 -18.7685198172017
*** budget = 50
VL50-1 0.013004374031251218 0.010281502368097458
RT-VL50-1 -7.144506333270233 0.966198539150814 -5.4495583721204195 -10.154237874887174
VL50-2 0.006875930516585275 0.006676316087788873
RT-VL50-2 -10.383815095454825 0.4580938827133992 -9.658786321287051 -11.461297722579062
VL50-3 0.000826683358430839 0.00020412148768716044
RT-VL50-3 -18.89851597875166 3.316486850014671 -7.615230780042776 -20.000024138165212
VL50-4 0.011961092128396888 0.009114528950880588
RT-VL50-4 -8.305016533527068 1.1676603631572675 -6.069978122435804 -11.600243943324456
VL50-5 0.02450617250683418 0.021738599788711243
RT-VL50-5 -6.858846323728121 1.725248993546939 -4.982934329583587 -13.72624628615529
VL50-all 0.01143485050829968 0.007834752770721005
RT-VL50-all -10.318140052946381 4.465836867496634 -6.858846323728121 -18.89851597875166
logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00010_10_dl_scale=1.0000,dr_cc=0.8000,seed=2_2024-05-30_19-34-07/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s2/05.30_19.34.38/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00010_10_dl_scale=1.0000,dr_cc=0.8000,seed=2_2024-05-30_19-34-07/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s2/05.30_19.34.38/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 -8.887653191350529 4.301660753800083 -4.544490470246298 -44.93500058497009
LR -0.04855260222396023 0.05925492281079825 -1.242385453359418e-05 -0.5501468157122877
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.04741482522571478 0.043676451432689965
RT-VL10-1 -7.417912188797172 1.563223987895681 -4.544490470246298 -17.433561970077594
VL10-2 0.025605742054770543 0.02488007509139916
RT-VL10-2 -8.174960394572105 3.710814609956228 -4.729999498707408 -34.36203152349573
VL10-3 0.01690330114106055 0.028426359843698963
RT-VL10-3 -14.413344955743456 6.3742127996325815 -4.782977826318853 -20.000024138165212
VL10-4 0.04005049846217234 0.037740970081500964
RT-VL10-4 -8.427747301760661 3.415989465244157 -5.356967256020467 -44.93500058497009
VL10-5 0.07667061401714813 0.06824847841823124
RT-VL10-5 -8.165884377091842 1.3164764695921298 -6.060756122958828 -20.000024138165212
VL10-all 0.04132899618017327 0.020647213474737938
RT-VL10-all -9.319969843593046 2.569019825603966 -7.417912188797172 -14.413344955743456
*** budget = 20
VL20-1 0.027354644436723887 0.02915716848856429
RT-VL20-1 -7.654408213391207 1.9855306716687495 -4.544490470246298 -17.433561970077594
VL20-2 0.014305695180211332 0.014744871199703675
RT-VL20-2 -8.46129378953755 4.484163264379728 -4.729999498707408 -34.36203152349573
VL20-3 0.005864814516431885 0.015342525603369312
RT-VL20-3 -17.05218758877716 5.287943774819711 -5.787541908892225 -20.000024138165212
VL20-4 0.01845051528981125 0.016568148083946937
RT-VL20-4 -8.183479498728719 0.8759613612689181 -6.250496898826067 -10.147776919189356
VL20-5 0.039169800903373436 0.03770092568754285
RT-VL20-5 -8.016138850237665 0.8961240257304277 -6.060756122958828 -9.643910005970989
VL20-all 0.02102909406531036 0.011409700849645248
RT-VL20-all -9.87350158813446 3.5988389786395727 -7.654408213391207 -17.05218758877716
*** budget = 30
VL30-1 0.014916998205088978 0.014149051676074897
RT-VL30-1 -7.397970083910151 1.7979416834827109 -4.544490470246298 -14.763582828979109
VL30-2 0.008882331618794892 0.008383891548234605
RT-VL30-2 -8.559865116097498 4.610263458285973 -4.729999498707408 -34.36203152349573
VL30-3 0.0011201819610086815 0.00196459434187113
RT-VL30-3 -18.299464025528906 4.261267692548377 -5.787541908892225 -20.000024138165212
VL30-4 0.011508175405316234 0.011491346881750084
RT-VL30-4 -8.144427723611464 0.8987779130964647 -6.250496898826067 -9.960611732162372
VL30-5 0.022926127974949466 0.019430627336269875
RT-VL30-5 -7.876540451587589 0.9143292510239435 -6.060756122958828 -9.413796715417558
VL30-all 0.01187076303303165 0.0071573434766535075
RT-VL30-all -10.055653480147122 4.139139854039509 -7.397970083910151 -18.299464025528906
*** budget = 40
VL40-1 0.01154029227237607 0.011051183010831548
RT-VL40-1 -7.87050144039396 2.5478456447505113 -5.39840331740134 -17.433561970077594
VL40-2 0.006120542124841972 0.004592817215457855
RT-VL40-2 -7.573034400172394 1.2777702727375582 -4.729999498707408 -10.098390659967142
VL40-3 0.0008167148149336108 0.00023361751147577578
RT-VL40-3 -18.34304632424675 4.251387258838919 -5.787541908892225 -20.000024138165212
VL40-4 0.009493923074421713 0.010952081585236362
RT-VL40-4 -8.039781751332042 0.8560077710640447 -6.513189611437 -9.960611732162372
VL40-5 0.02139488923924586 0.020158113906635407
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VL40-all 0.009873272305163844 0.006808446747472117
RT-VL40-all -9.98485755770506 4.183074247883173 -7.573034400172394 -18.34304632424675
*** budget = 50
VL50-1 0.008505689457686611 0.008757446396930905
RT-VL50-1 -7.977597769424829 2.730234273656724 -5.39840331740134 -17.433561970077594
VL50-2 0.005446116762623632 0.004124805926689029
RT-VL50-2 -8.276895069014518 3.4323396243323963 -4.729999498707408 -25.59742576722328
VL50-3 0.0007972102276006273 0.00025554054363850797
RT-VL50-3 -17.95641816766577 4.636972416840899 -5.787541908892225 -20.000024138165212
VL50-4 0.0066760430567974725 0.0070951758989168745
RT-VL50-4 -8.026130608371895 0.8791292246688395 -6.513189611437 -9.960611732162372
VL50-5 0.01754710119061637 0.013003515421045697
RT-VL50-5 -7.849856445672183 0.9182547114002401 -6.060756122958828 -9.413796715417558
VL50-all 0.007794432139064941 0.005501762160021533
RT-VL50-all -10.017379612029838 3.9719405551703835 -7.849856445672183 -17.95641816766577
logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00002_2_dl_scale=1.0000,dr_cc=0.8000,seed=1_2024-05-30_19-34-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s1/05.30_19.34.38/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/igsn/run_2024-05-30_19-34-06/run_1b708_00002_2_dl_scale=1.0000,dr_cc=0.8000,seed=1_2024-05-30_19-34-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0000_rc0.80_reg10_ds1.000_s1/05.30_19.34.38/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 -7.840925414476609 2.331968124848936 -5.812646130353181 -26.62962627466917
LR -0.023657749999712926 0.033961268133045446 -1.7547171521581362e-05 -0.40265257905997365
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.06262085350385484 0.06894321127163483
RT-VL10-1 -7.797951126622578 2.10481561158242 -6.0870060884134825 -24.508454387220286
VL10-2 0.06721293584551831 0.05616673892944522
RT-VL10-2 -8.502905962612003 2.960540593075611 -6.320768587458762 -24.088641398078057
VL10-3 0.05156189494756574 0.049861061173321314
RT-VL10-3 -8.492747082949435 2.9894866219855323 -6.3357802629058035 -24.088641398078057
VL10-4 0.043888976632918096 0.04183383868790854
RT-VL10-4 -7.9359879972721075 2.043466892261114 -6.0296981823341715 -26.62962627466917
VL10-5 0.031140180298217975 0.028146883288907302
RT-VL10-5 -6.696934193730332 0.8775017490787186 -5.812646130353181 -15.912840010683887
VL10-all 0.051284968245614994 0.012983006648927493
RT-VL10-all -7.885305272637291 0.6592179414666128 -6.696934193730332 -8.502905962612003
*** budget = 20
VL20-1 0.03247762847635966 0.04687787962875636
RT-VL20-1 -7.483807810635194 2.2204091387251848 -6.0870060884134825 -24.508454387220286
VL20-2 0.039634162326626816 0.04126858539701416
RT-VL20-2 -8.34292136850446 2.552357739169658 -6.320768587458762 -24.088641398078057
VL20-3 0.026659820671989395 0.02046682431950164
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VL20-4 0.022928873970939574 0.024947233322169495
RT-VL20-4 -7.67326869043729 0.7126566450514664 -6.295749820529516 -10.236683906506535
VL20-5 0.014955869485311772 0.011233964840863125
RT-VL20-5 -6.659275051312618 0.3633845316177478 -5.812646130353181 -7.398766727973436
VL20-all 0.027331270986245444 0.008376498529379512
RT-VL20-all -7.7765286324364284 0.71580911190115 -6.659275051312618 -8.723370241292578
*** budget = 30
VL30-1 0.018186846911923495 0.01938333740002207
RT-VL30-1 -7.680751036438491 2.632325102935827 -6.0870060884134825 -24.508454387220286
VL30-2 0.0278319310750507 0.03185939403624689
RT-VL30-2 -8.419474260648203 3.0073240036997033 -6.320768587458762 -24.088641398078057
VL30-3 0.020221385812377472 0.01604616115048807
RT-VL30-3 -8.52836727952296 2.548978284025749 -6.501156680010578 -22.791899588310773
VL30-4 0.015224352656801974 0.017929442618783487
RT-VL30-4 -7.948674888157223 1.7374931310488424 -6.295749820529516 -19.159237697142224
VL30-5 0.01078914770676807 0.008061226677504621
RT-VL30-5 -6.653976203085042 0.36806625693060946 -5.812646130353181 -7.398766727973436
VL30-all 0.01845073283258434 0.0056625284793498
RT-VL30-all -7.846248733570384 0.6713073226884133 -6.653976203085042 -8.52836727952296
*** budget = 40
VL40-1 0.012418689705441638 0.00985074477894447
RT-VL40-1 -7.7582562791068925 2.9745994647820995 -6.365722618233072 -24.508454387220286
VL40-2 0.017177790196307042 0.016687132682773217
RT-VL40-2 -9.2090234423921 3.81188934635812 -6.320768587458762 -24.088641398078057
VL40-3 0.015326497944468349 0.012840819930102014
RT-VL40-3 -8.843022492238601 2.791441721897077 -6.501156680010578 -22.791899588310773
VL40-4 0.009230227102236655 0.008775759247449815
RT-VL40-4 -7.7842777173855655 0.8262908896718287 -6.295749820529516 -10.236683906506535
VL40-5 0.00909867781839105 0.005852640191189631
RT-VL40-5 -6.6641093884273745 0.3659989406524705 -5.812646130353181 -7.2609641484200225
VL40-all 0.012650376553368948 0.0032256709367754297
RT-VL40-all -8.051737863910107 0.8998467013618966 -6.6641093884273745 -9.2090234423921
*** budget = 50
VL50-1 0.010085399135873726 0.007972207904212802
RT-VL50-1 -7.943195523810105 3.2748870331338917 -6.365722618233072 -24.508454387220286
VL50-2 0.013280601890521375 0.011177847884685725
RT-VL50-2 -8.65928402058134 2.7112036281193834 -6.320768587458762 -19.86210520650569
VL50-3 0.014349959656049464 0.01300599306244403
RT-VL50-3 -8.951868246237284 3.0342549891617443 -6.501156680010578 -22.791899588310773
VL50-4 0.007909758177609853 0.008266406656025595
RT-VL50-4 -7.827984169495079 0.7572709960484276 -6.358638397198705 -10.236683906506535
VL50-5 0.00762595872744994 0.005005262872701184
RT-VL50-5 -6.6717506409229905 0.34862695060471827 -6.023037488734706 -7.2609641484200225
VL50-all 0.010650335517500872 0.0027416160270534867
RT-VL50-all -8.01081652020936 0.7921093544188877 -6.6717506409229905 -8.951868246237284
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00016_16_clip_discriminator=0,dr_cc=0.8000,seed=5_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s5/04.03_09.42.30/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00016_16_clip_discriminator=0,dr_cc=0.8000,seed=5_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s5/04.03_09.42.30/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 -7.087455803367229 3.4491370312884673 -4.540778173303351 -64.75403834310245
LR -0.02701221682941302 0.047257867461456333 -2.1198294825275354e-05 -0.8717292138795092
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.06848672328876589 0.056527913159380126
RT-VL10-1 -6.739051527651988 1.0039931492018233 -5.0409225624861795 -10.850340473823275
VL10-2 0.026332389313368867 0.025476070174212877
RT-VL10-2 -7.386263709693536 4.514366049453522 -4.964093873896536 -38.95578391540664
VL10-3 0.022125018385205322 0.018482066847998517
RT-VL10-3 -7.022534011676659 3.8495410213781267 -4.960875567440743 -29.1521773570961
VL10-4 0.0343509208803673 0.02838985035539255
RT-VL10-4 -7.289883778061455 0.82144849967094 -5.153041768716548 -8.397347123344925
VL10-5 0.0840262897848714 0.0686003197086319
RT-VL10-5 -7.034223037588658 0.8089740169953588 -4.997529524405335 -8.302121956106172
VL10-all 0.04706426833051575 0.024651648241993798
RT-VL10-all -7.094391212934459 0.2273573645246633 -6.739051527651988 -7.386263709693536
*** budget = 20
VL20-1 0.03504455275470086 0.0302671481026308
RT-VL20-1 -6.793078234358586 1.1297917603178735 -5.117517879876652 -10.850340473823275
VL20-2 0.01270062977056857 0.011587583092870378
RT-VL20-2 -7.649202355888712 5.843234496687826 -4.964093873896536 -38.95578391540664
VL20-3 0.011386660644527093 0.009505474985421876
RT-VL20-3 -7.392088888881764 4.436193859810929 -4.960875567440743 -25.867828288113472
VL20-4 0.018288200826924967 0.016418925999814992
RT-VL20-4 -7.420218003347932 0.7499958472593801 -5.7419532763546 -8.397347123344925
VL20-5 0.048238271325707856 0.042494108835778935
RT-VL20-5 -6.980249091181563 0.7217153432152554 -5.277969241830606 -8.302121956106172
VL20-all 0.02513166306448587 0.01430013554498011
RT-VL20-all -7.246967314731711 0.31305686664887766 -6.793078234358586 -7.649202355888712
*** budget = 30
VL30-1 0.020935486762178733 0.019958813448198963
RT-VL30-1 -6.661334342012444 0.9000475420949009 -5.171157003294995 -10.550447628219153
VL30-2 0.008805307046421603 0.007499791729969908
RT-VL30-2 -7.920282552143569 6.135223254027782 -4.964093873896536 -38.95578391540664
VL30-3 0.008599811549223693 0.007590253453723306
RT-VL30-3 -7.6238647697842215 4.644761347362112 -4.960875567440743 -20.52417893340982
VL30-4 0.012570428340527923 0.011400464869863278
RT-VL30-4 -7.561478418924292 0.6475005550728926 -6.032023261250641 -8.397347123344925
VL30-5 0.033326088556934225 0.02858782530793339
RT-VL30-5 -6.8392159792156315 0.6396172196506018 -5.389758692368552 -8.019572265329582
VL30-all 0.016847424451057236 0.009372834052002073
RT-VL30-all -7.321235212416032 0.48496821801750906 -6.661334342012444 -7.920282552143569
*** budget = 40
VL40-1 0.020374671434661755 0.021329416849491252
RT-VL40-1 -6.751983108475631 1.1343083024518656 -5.289621613525616 -10.698377588752065
VL40-2 0.007723817666512834 0.0069472932223361514
RT-VL40-2 -8.126810168785271 6.860902298599188 -4.964093873896536 -38.95578391540664
VL40-3 0.006185308495654096 0.0047631012359243335
RT-VL40-3 -8.380044357276118 5.679879202621825 -4.960875567440743 -25.867828288113472
VL40-4 0.009264963632111694 0.007198593902648782
RT-VL40-4 -7.523087338718599 0.5815774377995917 -6.2750323017570455 -8.397347123344925
VL40-5 0.026414518410339104 0.025855629959825502
RT-VL40-5 -6.840896311582323 0.5793194445040084 -5.625363491116088 -8.019572265329582
VL40-all 0.013992655927855896 0.007970406150003401
RT-VL40-all -7.524564256967588 0.6570893412870094 -6.751983108475631 -8.380044357276118
*** budget = 50
VL50-1 0.01532671306009113 0.015085740436014374
RT-VL50-1 -6.602082200480668 0.6090315364235186 -5.364225389138903 -7.699109744466937
VL50-2 0.005651385710446107 0.0055774360279578356
RT-VL50-2 -8.541254024456988 7.632087264863315 -4.988436172344225 -38.95578391540664
VL50-3 0.004775536090521633 0.0042474910919386615
RT-VL50-3 -8.27569778581953 5.324092212940783 -4.960875567440743 -20.52417893340982
VL50-4 0.0075693311868321915 0.0060370089664012645
RT-VL50-4 -7.513059203442664 0.6261188948620341 -5.7419532763546 -8.35549837305588
VL50-5 0.019359538097886087 0.01370120892753757
RT-VL50-5 -6.906149058017016 0.48674908404266676 -5.841318361626267 -7.73808144423807
VL50-all 0.010536500829155429 0.005773201538002378
RT-VL50-all -7.567648454443374 0.7512817434508284 -6.602082200480668 -8.541254024456988
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00012_12_clip_discriminator=0,dr_cc=0.8000,seed=4_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s4/04.03_09.42.31/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00012_12_clip_discriminator=0,dr_cc=0.8000,seed=4_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s4/04.03_09.42.31/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 0.0009003059035035399 0.0
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.16090030590350357 0.0
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 0.0009003059035035399 0.0
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.16090030590350357 0.0
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 0.0009003059035035399 0.0
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.16090030590350357 0.0
RT-VL30-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-5 0.3209003059035036 0.0
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 0.0009003059035035399 0.0
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 0.0009003059035035399 0.0
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/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00008_8_clip_discriminator=0,dr_cc=0.8000,seed=3_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s3/04.03_09.42.28/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00008_8_clip_discriminator=0,dr_cc=0.8000,seed=3_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s3/04.03_09.42.28/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 -7.564855885389899 3.7490620826419123 -3.585735995824611 -33.725881142601565
LR -0.035493002285936055 0.05363172261995493 -7.504163002991504e-06 -0.588035095508227
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.26921544814549864 0.0987884711553748
RT-VL10-1 -8.699457790111737 5.8535107296970486 -4.301826913535209 -32.35262881461458
VL10-2 0.12894824413449674 0.07069008415224934
RT-VL10-2 -8.631132473775937 5.821061677071056 -4.243431999808058 -32.35262881461458
VL10-3 0.03268556340839226 0.029926679179182188
RT-VL10-3 -9.254826636731572 6.283771870545889 -4.243431999808058 -29.97381089789358
VL10-4 0.027031354801103424 0.025048057235858394
RT-VL10-4 -5.660836423875967 0.6658601053702551 -4.325980943706792 -11.356014623129733
VL10-5 0.023090728234392087 0.020320176444623885
RT-VL10-5 -6.643179972900944 1.153240147981022 -4.744380751620177 -11.679963805122112
VL10-all 0.09619426774477662 0.09504750746745996
RT-VL10-all -7.777886659479232 1.3804444523029225 -5.660836423875967 -9.254826636731572
*** budget = 20
VL20-1 0.20726398815340283 0.09303686691302716
RT-VL20-1 -8.251179715076608 5.398514763960368 -4.301826913535209 -32.35262881461458
VL20-2 0.08427129272804416 0.0578384558546183
RT-VL20-2 -8.14011456932907 5.305391614791904 -4.243431999808058 -32.35262881461458
VL20-3 0.016450178705067136 0.014719878400357538
RT-VL20-3 -10.945174838889237 7.340822690106173 -4.301826913535209 -29.97381089789358
VL20-4 0.015101627407844829 0.01448783950014233
RT-VL20-4 -5.711482656341286 0.7881339511209746 -4.325980943706792 -11.356014623129733
VL20-5 0.01102331784362764 0.010466598086374058
RT-VL20-5 -6.774312043914871 1.196699429641301 -5.444430063043453 -11.679963805122112
VL20-all 0.06682208096759731 0.07530503317200599
RT-VL20-all -7.964452764710214 1.7601183336165565 -5.711482656341286 -10.945174838889237
*** budget = 30
VL30-1 0.17661857451302299 0.07996444421463826
RT-VL30-1 -7.779882002505003 4.848363173200319 -5.334276234766081 -32.35262881461458
VL30-2 0.05908452171871609 0.039408987019895515
RT-VL30-2 -7.097092894755633 3.832581214588275 -4.243431999808058 -32.35262881461458
VL30-3 0.010064434438240255 0.00834065873557688
RT-VL30-3 -10.390729614381298 7.1358524790531215 -4.301826913535209 -29.97381089789358
VL30-4 0.010933986088359281 0.010233047404072146
RT-VL30-4 -5.659935054911513 0.3452880814936699 -4.394037341883968 -6.3094747257440735
VL30-5 0.007827791863606279 0.007461688550772512
RT-VL30-5 -6.658890914260394 0.6936734385035148 -5.674497777512997 -8.93233531172818
VL30-all 0.05290586172438898 0.06476429535406505
RT-VL30-all -7.517306096162768 1.5930343226483787 -5.659935054911513 -10.390729614381298
*** budget = 40
VL40-1 0.14499212495162592 0.07251891820384355
RT-VL40-1 -8.76238384978194 6.363279039044159 -5.334276234766081 -32.35262881461458
VL40-2 0.04474464609864427 0.03317561713503854
RT-VL40-2 -7.940211374147846 5.516884265392946 -4.243431999808058 -32.35262881461458
VL40-3 0.007879521188357516 0.006945067823185907
RT-VL40-3 -10.203746549960785 6.7190648856113855 -5.058502461344851 -27.82754598623671
VL40-4 0.007105117893071381 0.0052855082303509245
RT-VL40-4 -5.684787289279348 0.37973964734617105 -4.394037341883968 -6.3094747257440735
VL40-5 0.006047190690439028 0.005510990786621988
RT-VL40-5 -6.703836481654803 0.7121636583270843 -5.76346654968423 -8.93233531172818
VL40-all 0.04215372016442763 0.053458875977063364
RT-VL40-all -7.858993108964945 1.573666909878218 -5.684787289279348 -10.203746549960785
*** budget = 50
VL50-1 0.14004615151826588 0.07020802198102556
RT-VL50-1 -7.547837419758006 4.190890238805643 -5.334276234766081 -25.860191759710204
VL50-2 0.04245375767078419 0.028732402768240583
RT-VL50-2 -7.6264680158146465 4.84315613058314 -4.675013882098115 -32.35262881461458
VL50-3 0.006404487234389481 0.00629567230061273
RT-VL50-3 -10.846788183171634 7.120004067239789 -5.058502461344851 -27.82754598623671
VL50-4 0.0062320306170590145 0.005500963193861189
RT-VL50-4 -5.6023481186949535 0.39784514705439916 -4.394037341883968 -6.057533227402319
VL50-5 0.0044896346440552815 0.003703041564288175
RT-VL50-5 -6.637112112849084 0.719570111401422 -5.76346654968423 -8.93233531172818
VL50-all 0.03992521233691077 0.05204833460600678
RT-VL50-all -7.652110770057665 1.7577965443898882 -5.6023481186949535 -10.846788183171634
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00004_4_clip_discriminator=0,dr_cc=0.8000,seed=2_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s2/04.03_09.42.27/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00004_4_clip_discriminator=0,dr_cc=0.8000,seed=2_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s2/04.03_09.42.27/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 -7.274505424121463 3.564666046151997 -3.8901396787855633 -68.13181155836574
LR -0.022474060492970637 0.049595534336923486 -2.093293030114296e-05 -1.0080440133828352
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.16384321866215446 0.09907423083769114
RT-VL10-1 -8.902754016694125 5.01571965570603 -4.794699227783985 -32.137576107452
VL10-2 0.06305476071753081 0.05799530822278065
RT-VL10-2 -9.231449634176828 5.328747135581314 -4.5664895716748095 -32.137576107452
VL10-3 0.03692799264232367 0.03039393206823839
RT-VL10-3 -8.260742665641953 4.310631507234234 -4.8785817478662725 -27.202859636366384
VL10-4 0.022669968831322742 0.02113395623241862
RT-VL10-4 -6.8364500273462285 1.9242044005371608 -4.13931637702192 -22.85148586198217
VL10-5 0.024562020518487434 0.0210351426624249
RT-VL10-5 -6.180983658378579 1.2212200094050771 -4.484765624589487 -13.695032603451283
VL10-all 0.06221159227436381 0.05282042987995546
RT-VL10-all -7.882476000447542 1.182626677481278 -6.180983658378579 -9.231449634176828
*** budget = 20
VL20-1 0.10820094023560199 0.0719796630037546
RT-VL20-1 -9.2760473839804 5.605718157036159 -4.794699227783985 -32.137576107452
VL20-2 0.03347639768275292 0.031467824797438156
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VL20-3 0.02172248157576461 0.020342129507566812
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VL20-4 0.012212840454531195 0.012881872019726053
RT-VL20-4 -7.02272009050867 2.442192467087811 -4.13931637702192 -22.85148586198217
VL20-5 0.013152431237306871 0.011217865808513763
RT-VL20-5 -6.156467002447502 1.3095042420508 -4.560964765100024 -13.695032603451283
VL20-all 0.037753018237191516 0.03604349279640617
RT-VL20-all -8.219670582390396 1.4361579358495624 -6.156467002447502 -10.056065689866664
*** budget = 30
VL30-1 0.08100334570483143 0.057419333657710944
RT-VL30-1 -8.517502185219524 4.267795120193108 -5.265184896770675 -25.653886401780166
VL30-2 0.02379218550255799 0.022846952532359847
RT-VL30-2 -8.876135738221244 4.865258643503423 -4.5664895716748095 -32.137576107452
VL30-3 0.01396671594779144 0.012323829155713098
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VL30-4 0.008091767976116151 0.008352496971154035
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VL30-5 0.008921917283733816 0.007380141635443081
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VL30-all 0.027155186483006168 0.027498130165863055
RT-VL30-all -8.03038535416685 1.190752588299837 -6.08991500235308 -9.379860423512431
*** budget = 40
VL40-1 0.0626894880858282 0.04147932828881377
RT-VL40-1 -8.813671758512296 4.6585056780521645 -5.265184896770675 -25.653886401780166
VL40-2 0.01562922836962266 0.015066515158691458
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VL40-3 0.009920631937044988 0.009372573552379568
RT-VL40-3 -9.600501148519053 5.393748371791834 -4.8785817478662725 -25.142478795605054
VL40-4 0.005852321696182541 0.0041582484722332775
RT-VL40-4 -7.220453214194867 2.7169962476253526 -5.253595711793322 -22.85148586198217
VL40-5 0.007461374688285239 0.006540992704486112
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VL40-all 0.020310608955392724 0.021447523095702806
RT-VL40-all -8.313681968608481 1.4053066212864747 -6.159564519183607 -9.774219202632588
*** budget = 50
VL50-1 0.05781353935793212 0.041383836655640556
RT-VL50-1 -9.51696427354109 5.0483799895764845 -5.295991625833425 -25.653886401780166
VL50-2 0.012756596467184711 0.0138167924662629
RT-VL50-2 -9.52905238859285 5.663555293413577 -4.5664895716748095 -32.137576107452
VL50-3 0.00849923980182528 0.009532514133078908
RT-VL50-3 -10.382800993677016 5.708468082695023 -5.928416705577941 -25.142478795605054
VL50-4 0.005093586374540534 0.0036943829608377956
RT-VL50-4 -7.899768187131036 3.5794179429664736 -5.253595711793322 -22.85148586198217
VL50-5 0.006592558885220804 0.00633490220977138
RT-VL50-5 -5.8210108637909785 0.5398987451765952 -4.560964765100024 -6.687873494323453
VL50-all 0.01815110417734069 0.019997333343490187
RT-VL50-all -8.629919341346595 1.6180834699572841 -5.8210108637909785 -10.382800993677016
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00000_0_clip_discriminator=0,dr_cc=0.8000,seed=1_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s1/04.03_09.42.30/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00000_0_clip_discriminator=0,dr_cc=0.8000,seed=1_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr0_no1_es=n_sr0010_rc0.80_reg00_ds0.001_zt=p_g1_s1/04.03_09.42.30/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 -8.94111443086633 3.085502227917211 -5.414728688411869 -39.1942746385841
LR -0.04146236064055937 0.047076034248853 -7.018905151756627e-06 -0.4587136851228186
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.1217902094226155 0.07984992303867276
RT-VL10-1 -8.286355678230967 2.0589491826485045 -6.132904485823798 -24.52313891429485
VL10-2 0.04134589313182434 0.03596443213522937
RT-VL10-2 -8.309667425732698 2.215332514030476 -5.912620955743007 -24.52313891429485
VL10-3 0.022516859481009312 0.02175470355612073
RT-VL10-3 -9.258616386008734 3.6394568574783164 -6.432743061192828 -23.65433400398874
VL10-4 0.026680608204218116 0.027550177164810924
RT-VL10-4 -8.740607914672884 2.5154479940632184 -5.414728688411869 -22.355266394785353
VL10-5 0.04597705973462506 0.049283760546818885
RT-VL10-5 -8.915480923671252 2.879773423116109 -6.236400395795867 -39.1942746385841
VL10-all 0.05166212599485846 0.03613921835182775
RT-VL10-all -8.702145665663307 0.36974894779511247 -8.286355678230967 -9.258616386008734
*** budget = 20
VL20-1 0.07447997680125674 0.05799939481963233
RT-VL20-1 -8.023472648542322 1.4197148396693473 -6.171068967616618 -13.125488303592139
VL20-2 0.022720909949309025 0.0217053082461015
RT-VL20-2 -8.414964331194552 1.999312486034677 -6.171068967616618 -13.587325645752422
VL20-3 0.010218159137350729 0.00989394255273284
RT-VL20-3 -9.453028843069962 3.6398324892797786 -6.43428705849284 -20.000024138165212
VL20-4 0.012607970448610164 0.013531408931556963
RT-VL20-4 -8.415813475664034 1.6340727661494454 -6.467684946267473 -14.602098482461887
VL20-5 0.01967719110037494 0.024343327366865453
RT-VL20-5 -8.661450254912156 1.1075407492859586 -6.236400395795867 -12.371717331412386
VL20-all 0.027940841487380318 0.02370914221441051
RT-VL20-all -8.593745910676606 0.47576999363721323 -8.023472648542322 -9.453028843069962
*** budget = 30
VL30-1 0.04426077605674303 0.03231732434116737
RT-VL30-1 -7.884290818356034 1.3167757073873512 -6.2556851610994855 -11.180335517905203
VL30-2 0.01365142705760764 0.01029557248167094
RT-VL30-2 -8.10883415348594 1.9038159418842278 -6.070783791658474 -12.949179242029905
VL30-3 0.00727354400730116 0.007824940106736754
RT-VL30-3 -9.681466074717742 4.0456811190167725 -6.43428705849284 -20.000024138165212
VL30-4 0.007591185693529821 0.008344025289936262
RT-VL30-4 -8.64143710284704 1.8018519987645505 -6.536012996756069 -14.602098482461887
VL30-5 0.010877365312565295 0.01153888329017445
RT-VL30-5 -8.551373388543903 0.8460416932642513 -6.236400395795867 -10.34358005688157
VL30-all 0.016730859625549387 0.013961497467019381
RT-VL30-all -8.573480307590131 0.6202608326572357 -7.884290818356034 -9.681466074717742
*** budget = 40
VL40-1 0.03969509918379515 0.03215793303847498
RT-VL40-1 -7.801431324530986 0.9631403269560131 -6.267030495148416 -10.271283089153824
VL40-2 0.011080076552560509 0.00975328746004906
RT-VL40-2 -8.250909214889456 1.919775468343945 -6.171068967616618 -12.696286434686451
VL40-3 0.004772345492547872 0.006080364544929067
RT-VL40-3 -9.939790943642596 4.092030783180402 -6.781203508232581 -20.000024138165212
VL40-4 0.005832988975679646 0.008518389611125344
RT-VL40-4 -8.613235512526693 1.6755803298538914 -6.522005496534279 -14.602098482461887
VL40-5 0.009196155131617251 0.011886193358799483
RT-VL40-5 -8.530888796493983 0.679051971343051 -7.312606925541794 -9.97172129707031
VL40-all 0.014115333067240088 0.012989412371017722
RT-VL40-all -8.627251158416744 0.7149621650720785 -7.801431324530986 -9.939790943642596
*** budget = 50
VL50-1 0.031131792047760135 0.023621914625282927
RT-VL50-1 -8.210601981672143 1.4072089786131274 -6.267030495148416 -11.180335517905203
VL50-2 0.009183819964849878 0.007272538397498601
RT-VL50-2 -8.005683657202441 1.7009894693432992 -6.070783791658474 -12.696286434686451
VL50-3 0.003670214405084493 0.004107889276040738
RT-VL50-3 -10.121629973749465 4.516710019658397 -6.498371024295217 -20.000024138165212
VL50-4 0.003899685273155788 0.0035308287997978544
RT-VL50-4 -8.50495964772482 1.4470760269939473 -6.536012996756069 -12.982760974090375
VL50-5 0.005801168185034666 0.006008819995936674
RT-VL50-5 -8.537026915123437 0.7589565973128503 -6.813146317274169 -9.97172129707031
VL50-all 0.010737335975176992 0.010386632537570802
RT-VL50-all -8.675980435094463 0.7489103623799672 -8.005683657202441 -10.121629973749465
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00017_17_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s5/04.03_09.42.30/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00017_17_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s5/04.03_09.42.30/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00017_17_clip_discriminator=10,dr_cc=0.8000,seed=5_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s5/04.03_09.42.30/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 -19.440340332258536 2.8945354751464687 -4.321325549298626 -39.139221577108955
LR -0.19128806410595955 0.04308232675567066 -8.960920763900759e-05 -0.4266769580773472
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.29836166571310363 0.04140099489866662
RT-VL10-1 -17.975062503605052 4.771744028590963 -6.620365778751768 -21.969180749937284
VL10-2 0.13946145113153793 0.03752994225667959
RT-VL10-2 -17.975062503605052 4.771744028590963 -6.620365778751768 -21.969180749937284
VL10-3 0.0008893077246798694 6.559811397634887e-05
RT-VL10-3 -20.052247015582665 0.3175413773641053 -20.000024138165212 -22.462395762282267
VL10-4 0.12810828301216812 0.05197348689309416
RT-VL10-4 -17.171598972208724 6.182008175226496 -4.321325549298626 -39.139221577108955
VL10-5 0.2815512662254098 0.06679251471198558
RT-VL10-5 -17.16139139907968 6.20144192445927 -4.321325549298626 -39.139221577108955
VL10-all 0.16967439476137988 0.10973223527140583
RT-VL10-all -18.067072478816236 1.0564071782917097 -17.16139139907968 -20.052247015582665
*** budget = 20
VL20-1 0.2808753511322465 0.05113661525178844
RT-VL20-1 -16.509702584417113 5.799379369703689 -6.620365778751768 -21.969180749937284
VL20-2 0.12307492196911511 0.045589116168769275
RT-VL20-2 -16.509702584417113 5.799379369703689 -6.620365778751768 -21.969180749937284
VL20-3 0.0008783095458561989 9.145657570472035e-05
RT-VL20-3 -20.10446989300012 0.4429566511805288 -20.000024138165212 -22.462395762282267
VL20-4 0.10150950744496894 0.05904184394634545
RT-VL20-4 -14.781236145520744 7.498799083316627 -4.321325549298626 -39.139221577108955
VL20-5 0.2509685914518594 0.07645122733652417
RT-VL20-5 -14.770114947674259 7.516005582493177 -4.321325549298626 -39.139221577108955
VL20-all 0.15146133630880923 0.10259230257073318
RT-VL20-all -16.53504523100587 1.9459137766343195 -14.770114947674259 -20.10446989300012
*** budget = 30
VL30-1 0.2700875232339202 0.05399728316242779
RT-VL30-1 -15.631802790321418 6.2917414068524185 -6.671927404678479 -21.969180749937284
VL30-2 0.11338687948922319 0.04667317131285062
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VL30-3 0.0008724316571673274 0.00010724086913225635
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VL30-4 0.08011271844320794 0.0585954130954273
RT-VL30-4 -13.616788264851696 7.85994769385005 -4.321325549298626 -39.139221577108955
VL30-5 0.2224166792598262 0.08012791239619432
RT-VL30-5 -13.561931442384525 7.9146902585133 -4.321325549298626 -39.139221577108955
VL30-all 0.13737524641666896 0.0972954788395226
RT-VL30-all -15.70232334371168 2.366900531324348 -13.561931442384525 -20.06929143067935
*** budget = 40
VL40-1 0.25351071620994126 0.05706453538558344
RT-VL40-1 -14.415588144099825 6.18805914475425 -6.620365778751768 -21.96308859724457
VL40-2 0.09796930574413443 0.049016633675528644
RT-VL40-2 -14.415588144099825 6.18805914475425 -6.620365778751768 -21.96308859724457
VL40-3 0.0008626380030491987 0.00012317650094435104
RT-VL40-3 -20.093628587508643 0.38580212469316866 -20.000024138165212 -22.288865627706244
VL40-4 0.06925183796856708 0.05532563625878797
RT-VL40-4 -13.36033710902414 8.257865426847212 -4.321325549298626 -39.139221577108955
VL40-5 0.20804567610055605 0.07886457909810334
RT-VL40-5 -12.878860791357505 8.378991845246913 -4.321325549298626 -39.139221577108955
VL40-all 0.1259280348052496 0.0923643685651461
RT-VL40-all -15.032800555217989 2.600402783358357 -12.878860791357505 -20.093628587508643
*** budget = 50
VL50-1 0.2458588083388414 0.058633080904388374
RT-VL50-1 -14.708309626952465 6.101224871171647 -6.671927404678479 -21.950451432054688
VL50-2 0.09135773543101305 0.049632019127005836
RT-VL50-2 -14.708309626952465 6.101224871171647 -6.671927404678479 -21.950451432054688
VL50-3 0.000853848826276519 0.00013529364118074902
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VL50-4 0.06436709474699435 0.052502418707812096
RT-VL50-4 -12.680932827716841 8.034540678307424 -4.321325549298626 -39.139221577108955
VL50-5 0.20025274543615434 0.07836888621243827
RT-VL50-5 -12.083529271789404 8.139410107037945 -4.321325549298626 -39.139221577108955
VL50-all 0.12053804655585594 0.08987847364310719
RT-VL50-all -14.85931019581999 2.832776093935815 -12.083529271789404 -20.115469625688775
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00013_13_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s4/04.03_09.42.29/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00013_13_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s4/04.03_09.42.29/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00013_13_clip_discriminator=10,dr_cc=0.8000,seed=4_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s4/04.03_09.42.29/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 -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 0.0009003059035035399 0.0
RT-VL10-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL10-4 0.16090030590350357 0.0
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 0.0009003059035035399 0.0
RT-VL20-3 -20.00002413816521 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL20-4 0.16090030590350357 0.0
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 0.0009003059035035399 0.0
RT-VL30-3 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-4 0.16090030590350357 0.0
RT-VL30-4 -20.000024138165216 3.552713678800501e-15 -20.000024138165212 -20.000024138165212
VL30-5 0.3209003059035036 0.0
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 0.0009003059035035399 0.0
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 0.0009003059035035399 0.0
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/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00009_9_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s3/04.03_09.42.32/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00009_9_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s3/04.03_09.42.32/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00009_9_clip_discriminator=10,dr_cc=0.8000,seed=3_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s3/04.03_09.42.32/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 -7.93661449897418 2.8059533224263986 -3.844733845526515 -33.06256778880744
LR -0.04785066266587188 0.03579482600925442 -5.0104119460048224e-05 -0.37678728474815926
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.09322254207868719 0.08087845659894816
RT-VL10-1 -7.977839415108249 2.5466696377540154 -4.1941026447203695 -24.807090586088307
VL10-2 0.04747052142190686 0.042808863174133585
RT-VL10-2 -9.95741544959829 2.578074156117172 -6.3811210116079335 -30.308916044613937
VL10-3 0.03894222955405295 0.03695996801127377
RT-VL10-3 -10.484694039723928 2.134220028332656 -8.391718651528963 -27.677970383005448
VL10-4 0.03604836905866381 0.03424206502343732
RT-VL10-4 -8.706773524079404 1.3427995955177743 -5.597775846236699 -19.46334174072566
VL10-5 0.029902568672539934 0.027474994891612967
RT-VL10-5 -5.695055460945265 0.6875711883649744 -4.23286421703144 -9.210811694317103
VL10-all 0.04911724615717015 0.02276628723795887
RT-VL10-all -8.564355577891027 1.6867414061356722 -5.695055460945265 -10.484694039723928
*** budget = 20
VL20-1 0.04715930583104253 0.04719079831720977
RT-VL20-1 -6.9427978468480225 1.7878181582314712 -4.491545476825366 -13.69577989776053
VL20-2 0.023710565968444827 0.020003317843773294
RT-VL20-2 -10.215463247168522 3.031034824725583 -7.775401213809824 -30.308916044613937
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RT-VL20-3 -10.778599408374019 2.1394678968262584 -9.425334410349048 -20.000024138165212
VL20-4 0.017433677551967737 0.018287491150227323
RT-VL20-4 -8.786026047810605 0.807545950549254 -7.022437575062073 -10.457439891574728
VL20-5 0.015321994940326565 0.01273779198993877
RT-VL20-5 -5.723004286598086 0.44825242458910897 -4.700446005620342 -7.32632549379746
VL20-all 0.024818770237546826 0.011523736491375941
RT-VL20-all -8.48917816735985 1.9158621293809144 -5.723004286598086 -10.778599408374019
*** budget = 30
VL30-1 0.027654459630568035 0.023069378642650788
RT-VL30-1 -6.06820965359414 0.8155776611149735 -4.44279520236224 -7.907495329237337
VL30-2 0.017012501150570288 0.015824216856092095
RT-VL30-2 -10.515132031140235 3.6401812775769 -8.379228676263912 -30.308916044613937
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RT-VL30-3 -11.049236745611699 2.5609272731200035 -9.459736258206487 -20.000024138165212
VL30-4 0.012223786364778359 0.013203603004723315
RT-VL30-4 -8.802793851638349 0.7205260642309489 -7.186562253404522 -9.753125130702626
VL30-5 0.011901912668398578 0.010262277941831991
RT-VL30-5 -5.785468375589903 0.4197741489154402 -4.940926955299342 -7.32632549379746
VL30-all 0.01614907491250674 0.006069186079175793
RT-VL30-all -8.444168131514864 2.1871328009884 -5.785468375589903 -11.049236745611699
*** budget = 40
VL40-1 0.021976754925759935 0.022541838036178128
RT-VL40-1 -6.504326357660427 1.7432124163940912 -4.491545476825366 -13.69577989776053
VL40-2 0.011661837696753219 0.010317213916251924
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VL40-3 0.010404238957653057 0.011601191467406798
RT-VL40-3 -11.074776868217526 2.6756265395846417 -9.681975804846095 -20.000024138165212
VL40-4 0.006707131217576859 0.006339290231318716
RT-VL40-4 -8.84988994231083 0.6740810149723816 -7.537368723237492 -9.753125130702626
VL40-5 0.008797471350473736 0.00693540366059731
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VL40-all 0.011909486829643361 0.005299549649267009
RT-VL40-all -8.385488135678916 1.9652755113560407 -5.81077567820486 -11.074776868217526
*** budget = 50
VL50-1 0.014141136417398947 0.012052097272482066
RT-VL50-1 -5.959193308286111 0.4534224218679931 -4.94343611572546 -6.642714280269349
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VL50-4 0.005651953177979767 0.005779069800679915
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logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00005_5_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s2/04.03_09.42.30/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00005_5_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s2/04.03_09.42.30/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00005_5_clip_discriminator=10,dr_cc=0.8000,seed=2_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s2/04.03_09.42.30/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 -7.464741137886488 3.263766882282563 -4.564616292985429 -34.75833251569198
LR -0.02856409094114423 0.04533955859298634 -2.1267095046129114e-05 -0.6243838758916036
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0776239588638637 0.07127145736316387
RT-VL10-1 -7.575246670153249 3.589054552646685 -4.564616292985429 -34.75833251569198
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VL10-all 0.042771852298469255 0.020748071232267462
RT-VL10-all -7.7047573410909305 1.0129070284408161 -6.9034785498580185 -9.683787543717116
*** budget = 20
VL20-1 0.04146911275472855 0.03740442944317701
RT-VL20-1 -7.855692732491183 3.189920310567098 -4.564616292985429 -21.728499030185738
VL20-2 0.01705090720042146 0.01657277745305809
RT-VL20-2 -7.0204794403673665 3.628875226374964 -5.195379446769906 -29.717083127612092
VL20-3 0.007127778170945254 0.009962550990168954
RT-VL20-3 -12.222818959035882 6.788848340694688 -5.45178039248699 -21.23641795179113
VL20-4 0.014327990165180996 0.012467440055652977
RT-VL20-4 -6.998785444434431 1.879403336069202 -5.428920459200355 -21.273590038428065
VL20-5 0.027343488830599026 0.029133203004456848
RT-VL20-5 -7.122389117416888 0.9765836650042644 -5.018479558461068 -11.35753593454589
VL20-all 0.021463855424375056 0.011922233061689597
RT-VL20-all -8.244033138749149 2.0143165420786096 -6.998785444434431 -12.222818959035882
*** budget = 30
VL30-1 0.027063130116644983 0.02243701695704703
RT-VL30-1 -8.782611603425055 5.294457470189072 -4.564616292985429 -34.75833251569198
VL30-2 0.01070979568182909 0.009530461513146918
RT-VL30-2 -6.831938569160685 3.320934559786712 -5.210471282958069 -29.717083127612092
VL30-3 0.004100671368400121 0.006532781498073259
RT-VL30-3 -12.698776598907347 6.757079710317325 -5.45178039248699 -20.000024138165212
VL30-4 0.009918146846938038 0.008010346086403762
RT-VL30-4 -6.617147749915125 0.7128441939388912 -5.428920459200355 -8.628749090028345
VL30-5 0.018421613065723385 0.017495911517336135
RT-VL30-5 -7.132540513607513 0.9759902351730282 -5.018479558461068 -11.35753593454589
VL30-all 0.014042671415907124 0.007945698248405623
RT-VL30-all -8.412603007003145 2.274594911346904 -6.617147749915125 -12.698776598907347
*** budget = 40
VL40-1 0.0223849620924083 0.017707732424850322
RT-VL40-1 -8.957182939068103 5.668218561036355 -4.564616292985429 -34.75833251569198
VL40-2 0.00943044317201205 0.01179968417487159
RT-VL40-2 -6.5047089009709 0.5550943552047521 -5.49821638298728 -7.210752171347152
VL40-3 0.002632546750624466 0.004311500036055306
RT-VL40-3 -13.809445585766312 6.7355657774923445 -5.4569005994550555 -20.000024138165212
VL40-4 0.00893400228223427 0.008127010406682216
RT-VL40-4 -6.673312122076129 0.7474006096764033 -5.428920459200355 -8.628749090028345
VL40-5 0.014065762003673949 0.012954588050129657
RT-VL40-5 -7.133979087593698 1.096420865567198 -5.018479558461068 -11.35753593454589
VL40-all 0.011489543260190607 0.006550788114072025
RT-VL40-all -8.615725727095029 2.7392062629448 -6.5047089009709 -13.809445585766312
*** budget = 50
VL50-1 0.020188076095618273 0.015186410229484677
RT-VL50-1 -8.212930231463382 3.798486547647083 -4.564616292985429 -21.6834875389362
VL50-2 0.006830141912538834 0.007332585642475458
RT-VL50-2 -7.205107597024237 4.2180863736462815 -5.49821638298728 -29.717083127612092
VL50-3 0.002091219688441555 0.0034716353277534535
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VL50-4 0.006746618081976837 0.006189024314744148
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VL50-5 0.009447122748261797 0.007265942647064469
RT-VL50-5 -7.060219541108043 0.7750660976179262 -6.050894009725282 -9.834633149963222
VL50-all 0.00906063570536746 0.006047674621060613
RT-VL50-all -8.97592037753184 3.321136792046574 -6.849645683172311 -15.551698834891232
logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00001_1_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s1/04.03_09.42.26/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00001_1_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s1/04.03_09.42.26/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/abl/run_2024-04-03_09-41-50/run_eca63_00001_1_clip_discriminator=10,dr_cc=0.8000,seed=1_2024-04-03_09-41-50/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.80_reg00_ds0.001_zt=p_g1_s1/04.03_09.42.26/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 -10.627233145960432 5.4208073959864675 -4.588350057429372 -41.16437678124362
LR -0.05592703263166408 0.080902746004749 -1.3607023585660016e-06 -0.5050233052220429
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0416948162484655 0.04589435930910788
RT-VL10-1 -8.929421539124572 2.576651605040436 -4.816947316630996 -21.85831416319851
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VL10-5 0.07954003716872403 0.07593865316438965
RT-VL10-5 -8.55440008350085 3.244468448412317 -5.469853958444484 -23.86229100993218
VL10-all 0.044536671819363716 0.023633394951400818
RT-VL10-all -10.769932868051981 3.454535977922468 -8.55440008350085 -17.652001017461746
*** budget = 20
VL20-1 0.017558594852915766 0.013314998546126857
RT-VL20-1 -9.235312952811263 2.5081460239950055 -5.718972030102532 -19.07892671566674
VL20-2 0.018157394942666525 0.017651923047901478
RT-VL20-2 -9.019451995016516 4.4297220569148745 -4.746849446879534 -33.44515121743937
VL20-3 0.0016912550366436137 0.0031287156282191076
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VL20-4 0.03430890213072576 0.032128139195748674
RT-VL20-4 -8.395794717465003 3.502643030365478 -5.10387007262974 -26.132426206155387
VL20-5 0.03547576504299165 0.027029124462416725
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VL20-all 0.021438382401188665 0.012477700579955205
RT-VL20-all -10.706828696485555 4.340703154609857 -7.572592854582907 -19.31099096255209
*** budget = 30
VL30-1 0.011927032208585688 0.010353297869909692
RT-VL30-1 -9.141513009534657 2.364244342913816 -5.164120257862182 -15.235664843969834
VL30-2 0.011905117376591778 0.011934048358971211
RT-VL30-2 -8.88517284211134 4.18102411679032 -4.853291870665292 -27.08312103324487
VL30-3 0.0011068212303161173 0.0015435788644753772
RT-VL30-3 -19.89810023490977 1.7365537618537719 -8.348301523756849 -21.936375518837533
VL30-4 0.02001886638585461 0.019023534184372557
RT-VL30-4 -8.095089498825844 2.9283425707321507 -5.322972663712241 -22.333081774876014
VL30-5 0.02515939757259056 0.01986630910267059
RT-VL30-5 -7.577065881749416 0.44344841614119723 -6.895179622577656 -9.754532152924174
VL30-all 0.014023446954787749 0.008193606597983486
RT-VL30-all -10.719388293426206 4.623070926134665 -7.577065881749416 -19.89810023490977
*** budget = 40
VL40-1 0.00903698904738 0.008515010687365338
RT-VL40-1 -8.91229334443209 2.453407664798876 -5.718972030102532 -15.02241926170277
VL40-2 0.008431664566938774 0.008079393975416306
RT-VL40-2 -8.662342305184906 3.4454843369879637 -4.853291870665292 -20.04085681462733
VL40-3 0.0011217722610593287 0.001762263003640595
RT-VL40-3 -19.825764353643162 2.0023667446895583 -8.348301523756849 -21.936375518837533
VL40-4 0.014785770579723388 0.01361201995467299
RT-VL40-4 -7.856866158634432 2.453345767426095 -5.322972663712241 -22.333081774876014
VL40-5 0.020507093890055273 0.017902253403581567
RT-VL40-5 -7.557064738708846 0.48164264978563365 -6.895179622577656 -9.954200631940584
VL40-all 0.010776658069031353 0.006519150668379774
RT-VL40-all -10.562866180120688 4.658217313085278 -7.557064738708846 -19.825764353643162
*** budget = 50
VL50-1 0.009085946677036454 0.009443026925815614
RT-VL50-1 -8.881079531549384 2.3401154028611413 -5.718972030102532 -15.02241926170277
VL50-2 0.006906483711263097 0.008102577212684222
RT-VL50-2 -8.928689021464901 3.769323857707953 -4.853291870665292 -20.04085681462733
VL50-3 0.0008200774349921799 0.00019856725535408382
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VL50-4 0.01155272180670414 0.010669428272917303
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VL50-5 0.017943313659085178 0.015841852298069392
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VL50-all 0.009261708657816208 0.005611739858035955
RT-VL50-all -10.78279448724711 4.749481467603713 -7.537573575854526 -20.228687117175365
logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00037_37_dl_scale=0.5000,dr_cc=0.9000,seed=5_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s5/04.02_18.17.56/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00037_37_dl_scale=0.5000,dr_cc=0.9000,seed=5_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s5/04.02_18.17.56/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00037_37_dl_scale=0.5000,dr_cc=0.9000,seed=5_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s5/04.02_18.17.56/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 -8.963548646577705 5.054346958836997 -4.748703310823736 -87.37169150579518
LR -0.03204629947709999 0.06962887097443365 -1.1872333039786298e-05 -1.0305789071806046
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.04742347090975654 0.04232457589368227
RT-VL10-1 -8.129252621655672 4.407534716492694 -4.7568836138757185 -39.118591429399444
VL10-2 0.047170075382841466 0.04016910532922581
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VL10-3 0.037310989051749385 0.03657698491436279
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VL10-4 0.044808651940452686 0.03606341087929151
RT-VL10-4 -10.001992245531076 4.950298729003953 -7.004712957233548 -45.69362730657832
VL10-5 0.05424273960644541 0.0468392954626111
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VL10-all 0.046191185378249094 0.005441940173608004
RT-VL10-all -9.179903747458578 0.6351626345697619 -8.129252621655672 -10.001992245531076
*** budget = 20
VL20-1 0.027070374634168875 0.025947734841681454
RT-VL20-1 -7.378642013634064 1.163681594137806 -4.8495542041669015 -12.910267567043224
VL20-2 0.027281682408108653 0.020486783982612323
RT-VL20-2 -9.786436382041915 5.976709778571064 -5.731472599021313 -36.71613449316961
VL20-3 0.019750748990861714 0.018678314194405
RT-VL20-3 -9.025618133485708 3.312261331819887 -7.324886309342472 -36.796096188026695
VL20-4 0.02444379975541886 0.019268660974300977
RT-VL20-4 -9.804162208592928 4.314877756252122 -7.004712957233548 -45.69362730657832
VL20-5 0.0320991370430678 0.024698098009753058
RT-VL20-5 -8.691553818950348 4.387932985744596 -6.036472233640236 -45.790560491422816
VL20-all 0.02612914856632518 0.004034337242680146
RT-VL20-all -8.937282511340992 0.8910767283606701 -7.378642013634064 -9.804162208592928
*** budget = 30
VL30-1 0.019391312117867585 0.01488049956190517
RT-VL30-1 -7.290401621180498 1.0929297655617067 -4.84717472282791 -9.949888348188448
VL30-2 0.01861106107535014 0.01614365544929404
RT-VL30-2 -9.947743430327332 6.442359251753726 -5.731472599021313 -36.71613449316961
VL30-3 0.013681019010632062 0.013791746398953612
RT-VL30-3 -9.644848131070166 6.909881117413262 -7.324886309342472 -57.717999782627004
VL30-4 0.01843116774699126 0.016895372567013008
RT-VL30-4 -10.524690333837263 6.2226482072759115 -7.004712957233548 -45.69362730657832
VL30-5 0.02344589389783575 0.019102019545948045
RT-VL30-5 -8.96286790302113 5.286595878317568 -6.527576803592777 -45.790560491422816
VL30-all 0.018712090769735356 0.0031071221889225273
RT-VL30-all -9.274110283887278 1.1123983555178951 -7.290401621180498 -10.524690333837263
*** budget = 40
VL40-1 0.014923075659663488 0.011443032702336783
RT-VL40-1 -7.068486792929308 0.9490542535160681 -4.938353910133811 -8.786395122645391
VL40-2 0.014186880545078148 0.011590908569522312
RT-VL40-2 -9.13723482337747 4.835899957908106 -5.731472599021313 -36.71613449316961
VL40-3 0.010156115159520787 0.009381494305499874
RT-VL40-3 -8.741913750984768 0.8089942217253123 -7.324886309342472 -11.573778623597695
VL40-4 0.013909934217633506 0.01160464367610798
RT-VL40-4 -10.477133081563801 5.968708882158336 -7.835323064043988 -45.69362730657832
VL40-5 0.01720081255652274 0.01099776564734971
RT-VL40-5 -9.263441128958265 6.102415554977511 -6.962695358110591 -45.790560491422816
VL40-all 0.014075363627683735 0.002275405817155105
RT-VL40-all -8.937641915562722 1.0998241580698482 -7.068486792929308 -10.477133081563801
*** budget = 50
VL50-1 0.012241847343649552 0.010029006514314712
RT-VL50-1 -7.446671525168892 1.4077349366154668 -4.938353910133811 -12.910267567043224
VL50-2 0.012994538709734793 0.01252027074249406
RT-VL50-2 -10.34467604406675 6.967120993220678 -6.670327047287099 -36.71613449316961
VL50-3 0.008832558398979147 0.009419642026422806
RT-VL50-3 -8.742019884407522 0.8421914324825525 -7.324886309342472 -11.573778623597695
VL50-4 0.011983683049897997 0.009397950019374856
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VL50-5 0.01469115191258672 0.010350270141605966
RT-VL50-5 -9.395645796130417 6.779164813936981 -6.527576803592777 -45.790560491422816
VL50-all 0.012148755882969643 0.0019085181149026715
RT-VL50-all -9.298154998384005 1.1347854488113955 -7.446671525168892 -10.56176174214645
logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00029_29_dl_scale=0.5000,dr_cc=0.9000,seed=4_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s4/04.02_18.17.59/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00029_29_dl_scale=0.5000,dr_cc=0.9000,seed=4_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s4/04.02_18.17.59/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00029_29_dl_scale=0.5000,dr_cc=0.9000,seed=4_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s4/04.02_18.17.59/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
RT -8.691663539731055 3.495818860984459 -4.875293057598824 -39.2105535635151
LR -0.032276740706029275 0.04867755469409586 -2.9685667259915505e-06 -0.48549887562531113
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.034317803145777266 0.03731561996317181
RT-VL10-1 -6.8358728912912525 0.4676193560869498 -6.158056650876622 -8.733080123586374
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VL10-4 0.059407155025158837 0.046885759945710676
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VL10-5 0.05691518442686738 0.06845131941527192
RT-VL10-5 -8.478811920146002 1.0857234588308202 -7.1722939343243315 -20.000024138165212
VL10-all 0.04613160759386782 0.01061711305870731
RT-VL10-all -8.963061370358645 1.7801649609615446 -6.8358728912912525 -11.777436591789437
*** budget = 20
VL20-1 0.01441601428472741 0.01507298729968529
RT-VL20-1 -6.7211620470892255 0.32900490020796996 -6.231945636678963 -7.740943045870496
VL20-2 0.023127648252616442 0.020192805995293484
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VL20-4 0.033158209024333016 0.027118047856125833
RT-VL20-4 -10.01321530078754 4.799711720784771 -7.657617874004486 -34.90744329448127
VL20-5 0.023188201018278898 0.018958576539184136
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VL20-all 0.021675789361975506 0.006936819557211007
RT-VL20-all -9.040117654562982 2.0809838461939214 -6.7211620470892255 -12.598226155330735
*** budget = 30
VL30-1 0.008625873508793007 0.008960540362629657
RT-VL30-1 -6.703761987433531 0.35754601381444084 -6.231945636678963 -7.740943045870496
VL30-2 0.014355337047682841 0.011808307394996467
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VL30-3 0.008068676246627451 0.011904060962204038
RT-VL30-3 -14.24259370257029 5.673742597646317 -7.859188358671488 -20.017717310976046
VL30-4 0.02408266659749076 0.02082457643130451
RT-VL30-4 -9.448538142335982 3.835562509422329 -7.657617874004486 -32.346299903859794
VL30-5 0.01663025289309198 0.013064949668393742
RT-VL30-5 -8.29117653196466 0.35823057998919355 -7.474751801411563 -9.115693645468616
VL30-all 0.014352561258737207 0.005867623031862783
RT-VL30-all -9.276514686776888 2.637344016322124 -6.703761987433531 -14.24259370257029
*** budget = 40
VL40-1 0.008625431894582508 0.009901693095424546
RT-VL40-1 -6.736922256141273 0.36377017186663674 -6.231945636678963 -7.740943045870496
VL40-2 0.01048979422993282 0.0068741577737944535
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VL40-3 0.006116276588915565 0.009199790640154365
RT-VL40-3 -14.932156962324454 5.818713197845158 -7.937880278904736 -20.15454975885851
VL40-4 0.021772223467312547 0.02023769897626444
RT-VL40-4 -9.579565611265942 4.232706860524556 -7.657617874004486 -32.346299903859794
VL40-5 0.013952527901508735 0.012256285288066892
RT-VL40-5 -8.307398140645734 0.4358224327221462 -7.219475384660776 -9.674335323172912
VL40-all 0.012191250816450436 0.005429778354067275
RT-VL40-all -9.431839464386815 2.903320577694163 -6.736922256141273 -14.932156962324454
*** budget = 50
VL50-1 0.005818664112975625 0.005944118364865304
RT-VL50-1 -6.780106892822441 0.36189155314651394 -6.273537196148975 -7.740943045870496
VL50-2 0.011288650300297073 0.0075611761065196795
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VL50-3 0.0033132592687023176 0.005321021386185811
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VL50-4 0.0149377378488889 0.013843833443574573
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VL50-5 0.011152477931784872 0.009113410655847977
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VL50-all 0.009302157892529757 0.004174475617774709
RT-VL50-all -9.51803806381802 3.2186345794826736 -6.780106892822441 -15.776450563809295
logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00021_21_dl_scale=0.5000,dr_cc=0.9000,seed=3_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s3/04.02_18.17.53/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00021_21_dl_scale=0.5000,dr_cc=0.9000,seed=3_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s3/04.02_18.17.53/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00021_21_dl_scale=0.5000,dr_cc=0.9000,seed=3_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s3/04.02_18.17.53/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 -10.180626701524037 3.150033115293237 -5.236540660724592 -37.690146616519016
LR -0.039254352789366245 0.04812440062554465 -3.648197822703203e-05 -0.6712524185873686
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0465774691232958 0.05490169270292981
RT-VL10-1 -9.871059088853562 2.9764395589213457 -6.426387545909112 -37.690146616519016
VL10-2 0.04363837619727775 0.04405718142938471
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VL10-4 0.044208242464776504 0.03906106607280066
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VL10-5 0.02896300346685149 0.03302414575353339
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VL10-all 0.04502027500746753 0.010406918785926185
RT-VL10-all -10.78566570168404 1.093852703211044 -9.270157786142896 -12.234640723344233
*** budget = 20
VL20-1 0.020638991120101625 0.01812908663320154
RT-VL20-1 -9.258876695629926 1.204687996655409 -6.521903793294922 -12.34385038981973
VL20-2 0.020908062062600416 0.018477722490131958
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VL20-3 0.030671286608611997 0.02679459819439567
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VL20-4 0.023477427495481096 0.021558889145744588
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VL20-5 0.012789812310838285 0.010689639249770778
RT-VL20-5 -9.283861909671565 1.443556321974608 -7.16986087551074 -13.704918246716016
VL20-all 0.021697115919526683 0.005740863010418506
RT-VL20-all -10.86844762595457 1.553856826579815 -9.258876695629926 -13.292243679281347
*** budget = 30
VL30-1 0.015023082263456975 0.016030247409796687
RT-VL30-1 -9.317319444098322 1.1386723265551133 -6.521903793294922 -12.34385038981973
VL30-2 0.014962807673576111 0.01424323112507689
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VL30-3 0.02236410646432062 0.022336359601655763
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VL30-4 0.016686603983980176 0.014239704431837967
RT-VL30-4 -11.021570441100963 2.8349538200940074 -8.248310770367068 -23.50325021318751
VL30-5 0.008623603424404318 0.007811045661743399
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VL30-all 0.01553204076194764 0.004389070733174777
RT-VL30-all -10.90524539563096 1.4513906058967658 -9.317319444098322 -13.203031179445818
*** budget = 40
VL40-1 0.010626901912033677 0.011929247125561199
RT-VL40-1 -9.274121096407963 1.203464446263586 -6.521903793294922 -12.095667765543332
VL40-2 0.010395606913490162 0.0107391008989858
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VL40-3 0.016395249271367693 0.016068824055001523
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VL40-4 0.012423412337571764 0.013055808627690018
RT-VL40-4 -10.830785936944142 2.6162179383863533 -8.399512987309816 -23.50325021318751
VL40-5 0.007559828562693 0.006613661900584963
RT-VL40-5 -9.732158496071632 1.5148754746595567 -7.510424808013314 -13.704918246716016
VL40-all 0.011480199799431259 0.002909334317344913
RT-VL40-all -11.015130810817832 1.5338040538890274 -9.274121096407963 -13.595249640841779
*** budget = 50
VL50-1 0.00682134989402229 0.0056994736067114814
RT-VL50-1 -9.321434053469575 1.1285804015512788 -6.521903793294922 -12.095667765543332
VL50-2 0.009456374467661965 0.00942672444424113
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VL50-3 0.011862411859362817 0.012694697232778007
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VL50-4 0.011004175547454106 0.013320173250290652
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VL50-5 0.005736972085490799 0.005689266752212596
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VL50-all 0.008976256770798396 0.002358365861474676
RT-VL50-all -10.998607793568643 1.7231000579728635 -9.321434053469575 -14.109860687495308
logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00013_13_dl_scale=0.5000,dr_cc=0.9000,seed=2_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s2/04.02_18.18.00/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00013_13_dl_scale=0.5000,dr_cc=0.9000,seed=2_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s2/04.02_18.18.00/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00013_13_dl_scale=0.5000,dr_cc=0.9000,seed=2_2024-04-02_18-17-06/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s2/04.02_18.18.00/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 -9.186763451053281 3.464480166070301 -5.414816010248788 -42.608221977018935
LR -0.03133980985258414 0.052789528120514496 -9.952933547774379e-06 -0.5741390246518634
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.04974228889544237 0.04340838727530866
RT-VL10-1 -7.405785026404009 1.8473744221952595 -5.624015960716642 -26.095153060413544
VL10-2 0.040411618784576185 0.037647589734123486
RT-VL10-2 -9.140721250695352 4.5739738451478305 -5.414816010248788 -34.446302959525504
VL10-3 0.04784687446728648 0.05007702269025624
RT-VL10-3 -11.649983398742782 5.13795813922879 -6.087042332931743 -29.102280749269415
VL10-4 0.050560450477201306 0.04409380323658847
RT-VL10-4 -10.023302336708948 3.8430638332380282 -6.304488228685127 -42.608221977018935
VL10-5 0.03964531194193896 0.03781200671608709
RT-VL10-5 -7.834196075487443 1.0396538238610822 -6.049480387383969 -12.247684188601232
VL10-all 0.04564130891328906 0.004672946575247947
RT-VL10-all -9.210797617607707 1.5341319467910655 -7.405785026404009 -11.649983398742782
*** budget = 20
VL20-1 0.029659401707581683 0.028345000494737784
RT-VL20-1 -7.169802023499942 0.9347858524941596 -5.624015960716642 -12.162190279738349
VL20-2 0.021449122212838637 0.021002499960509333
RT-VL20-2 -9.570197041067418 5.076166258240958 -6.115992412400874 -33.173659536713615
VL20-3 0.02093513107787283 0.029824191049975097
RT-VL20-3 -13.417647627817155 5.744998511433832 -6.327194627960379 -29.04789292302121
VL20-4 0.025888425149381136 0.025672833718606715
RT-VL20-4 -10.691482154851217 4.543378768069765 -6.48635229043071 -42.608221977018935
VL20-5 0.019280792887979563 0.0153654007983106
RT-VL20-5 -7.522042137891904 0.8086009942720146 -6.305771273115926 -10.519523255539148
VL20-all 0.023442574607130773 0.0038003335814245317
RT-VL20-all -9.674234197025527 2.2787221165618 -7.169802023499942 -13.417647627817155
*** budget = 30
VL30-1 0.019261363954419658 0.016858275781486767
RT-VL30-1 -7.093966504532554 0.9530161040439249 -5.624015960716642 -12.162190279738349
VL30-2 0.013686770223234496 0.012582809136108996
RT-VL30-2 -10.199958297977565 6.164261259662073 -6.115992412400874 -34.446302959525504
VL30-3 0.014421311524594645 0.02140483312336034
RT-VL30-3 -13.864559393481457 5.659092653688753 -6.087042332931743 -21.781844613710966
VL30-4 0.018485365350888155 0.019933760642769797
RT-VL30-4 -11.03007473461457 5.261578091813737 -7.093817914380232 -42.608221977018935
VL30-5 0.013728072467052339 0.010109636669178657
RT-VL30-5 -7.575880562607361 0.8339464501999038 -6.305771273115926 -10.519523255539148
VL30-all 0.015916576704037858 0.002440642643096085
RT-VL30-all -9.952887898642702 2.463580506442571 -7.093966504532554 -13.864559393481457
*** budget = 40
VL40-1 0.013647563413202328 0.01178886009892087
RT-VL40-1 -6.945308208504383 0.6097226558489285 -5.624015960716642 -7.829818638683507
VL40-2 0.009974795100046006 0.010527695737381677
RT-VL40-2 -9.507219064576185 4.652959172007721 -6.478010814701763 -28.83674849969055
VL40-3 0.008190631492213461 0.014450784701287146
RT-VL40-3 -15.282379278871245 5.6174860568700025 -6.087042332931743 -21.781844613710966
VL40-4 0.013832718685191097 0.01518651137415454
RT-VL40-4 -10.243763055040917 3.1123380976591055 -6.48635229043071 -25.556953928125967
VL40-5 0.011119358790299818 0.00922250543106547
RT-VL40-5 -7.497954513074072 0.8479859716936741 -6.305771273115926 -10.519523255539148
VL40-all 0.011353013496190542 0.0021618805496210593
RT-VL40-all -9.895324824013361 2.9577820548683524 -6.945308208504383 -15.282379278871245
*** budget = 50
VL50-1 0.01292729172131673 0.011966345651571309
RT-VL50-1 -7.063253985879812 1.1090991636579677 -5.624015960716642 -12.162190279738349
VL50-2 0.007423066516565398 0.005853067496033394
RT-VL50-2 -10.546171944131398 6.77696632141421 -6.478010814701763 -34.446302959525504
VL50-3 0.007266456178548549 0.015395747906741216
RT-VL50-3 -16.284901859050198 5.118370040763421 -6.327194627960379 -21.781844613710966
VL50-4 0.01052451555365787 0.011101782356336357
RT-VL50-4 -10.562172490537153 3.2455070691457704 -7.285047116746577 -25.556953928125967
VL50-5 0.010632571335971433 0.008489286012219653
RT-VL50-5 -7.410752037420138 0.730621561270025 -6.374658711310304 -9.331658140733259
VL50-all 0.009754780261211996 0.0021473930372987934
RT-VL50-all -10.373450463403739 3.3089481410157546 -7.063253985879812 -16.284901859050198
logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00005_5_dl_scale=0.5000,dr_cc=0.9000,seed=1_2024-04-02_18-17-05/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s1/04.02_18.17.58/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00005_5_dl_scale=0.5000,dr_cc=0.9000,seed=1_2024-04-02_18-17-05/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s1/04.02_18.17.58/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/sn/run_2024-04-02_18-17-05/run_bd253_00005_5_dl_scale=0.5000,dr_cc=0.9000,seed=1_2024-04-02_18-17-05/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg10_ds0.500_zt=p_g1_s1/04.02_18.17.58/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 -8.381985324096803 2.7324274366558994 -4.8850561734664195 -28.13250401678992
LR -0.02884185548644019 0.04935393049994466 -3.972415044040112e-05 -0.7299926694945149
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.03826046297274632 0.04050020940271956
RT-VL10-1 -7.194070773433632 0.4537373840879984 -6.227602190174238 -8.313633765688108
VL10-2 0.06350910432909025 0.050271673051267646
RT-VL10-2 -7.42470445935367 0.6427694780944765 -6.337418555651007 -12.53590124729389
VL10-3 0.030468585271079967 0.04114173140228435
RT-VL10-3 -8.36941763995602 2.6685038130800707 -5.37861175150238 -20.000024138165212
VL10-4 0.06480849422200165 0.04276073085694936
RT-VL10-4 -8.833202098571327 1.3299650069190139 -7.03127851505078 -19.98379714125466
VL10-5 0.040800632466888254 0.037329653451408686
RT-VL10-5 -9.210366994999596 3.831084550431709 -5.449347165173662 -22.4406432385622
VL10-all 0.04756945585236129 0.013972622735662781
RT-VL10-all -8.206352393262849 0.7827213704318519 -7.194070773433632 -9.210366994999596
*** budget = 20
VL20-1 0.01917731614768937 0.020290483045884974
RT-VL20-1 -7.210279043297618 0.43065139312114553 -6.227602190174238 -8.114554118837114
VL20-2 0.03470787542981699 0.030434391238103917
RT-VL20-2 -7.498445451620096 0.5016460678432603 -6.49272851044875 -9.817093797107848
VL20-3 0.012251795662501109 0.012693203602234684
RT-VL20-3 -8.82706141655937 3.4423378921298493 -5.37861175150238 -20.000024138165212
VL20-4 0.03963990952359092 0.030422575419431698
RT-VL20-4 -8.998379430298815 1.4252404549681508 -7.03127851505078 -19.98379714125466
VL20-5 0.020837519055414346 0.017663422503075538
RT-VL20-5 -9.382439732705878 4.319356781205951 -5.449347165173662 -22.4406432385622
VL20-all 0.025322883163802545 0.010215626630031659
RT-VL20-all -8.383321014896357 0.8639991713327638 -7.210279043297618 -9.382439732705878
*** budget = 30
VL30-1 0.01348013085177995 0.013389470413171308
RT-VL30-1 -7.246380738576867 0.3782961078568864 -6.397290055119383 -7.888424738877689
VL30-2 0.02290959606366101 0.0200463653800937
RT-VL30-2 -7.393949580515105 0.4384521323979406 -6.49272851044875 -8.920042501013825
VL30-3 0.008073036011687169 0.009127728503380783
RT-VL30-3 -8.987229244725409 3.835542006653703 -6.664288630562955 -20.000024138165212
VL30-4 0.02828789443888871 0.024995620703371354
RT-VL30-4 -9.12244849655547 1.6473979237919676 -7.298434544983453 -19.98379714125466
VL30-5 0.014828258174751023 0.012760147635605285
RT-VL30-5 -9.369359083389163 4.355931414200367 -5.840998647728982 -22.4406432385622
VL30-all 0.017515783108153576 0.007180617701153722
RT-VL30-all -8.423873428752403 0.9106648332350734 -7.246380738576867 -9.369359083389163
*** budget = 40
VL40-1 0.0086390214660548 0.00762848884882138
RT-VL40-1 -7.28161337537493 0.34638014874648443 -6.554835524124011 -7.888424738877689
VL40-2 0.019606001237203786 0.01716805161687072
RT-VL40-2 -7.449666360351425 0.44180024603956386 -6.556653535512835 -8.920042501013825
VL40-3 0.00451687524575586 0.004814129902515271
RT-VL40-3 -9.250151465052785 4.352919171436624 -6.664288630562955 -20.000024138165212
VL40-4 0.021304160818833837 0.018838678935337687
RT-VL40-4 -9.209785411028845 1.8727050328174424 -7.84639417368229 -19.98379714125466
VL40-5 0.01078343935510941 0.011446471694078603
RT-VL40-5 -9.496979310205166 4.651633792998712 -5.840998647728982 -22.4406432385622
VL40-all 0.012969899624591538 0.006457375466086398
RT-VL40-all -8.53763918440263 0.963438994004043 -7.28161337537493 -9.496979310205166
*** budget = 50
VL50-1 0.006387682340415531 0.005129457565359421
RT-VL50-1 -7.292881147367443 0.3283024196492152 -6.600072700451612 -7.888424738877689
VL50-2 0.01568396695573285 0.012117426142940799
RT-VL50-2 -7.47158861863949 0.4540301961480697 -6.785560792566619 -8.920042501013825
VL50-3 0.003290057224505538 0.0038754735302571008
RT-VL50-3 -9.722569221357976 4.713256720654619 -6.664288630562955 -20.000024138165212
VL50-4 0.018761612724836473 0.022345790494684558
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logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00038_38_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=5_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s5/04.02_05.59.22/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00038_38_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=5_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s5/04.02_05.59.22/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00038_38_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=5_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s5/04.02_05.59.22/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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*** budget = 20
VL20-1 0.08001789157635576 0.0453576859904974
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VL20-4 0.018068934584221808 0.015647285923858167
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VL20-5 0.022678208417680912 0.021569448507826817
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RT-VL20-all -9.256165172018658 0.11487797124880869 -9.123717726911071 -9.424540855226022
*** budget = 30
VL30-1 0.06718089868383575 0.03377038333032564
RT-VL30-1 -9.407757184386957 3.270149086242265 -6.028604919408093 -21.127547440243326
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VL30-3 0.009684170530638397 0.008164799095059825
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VL30-4 0.014394784202155042 0.015367228384703153
RT-VL30-4 -9.068672508451622 1.8531239895773541 -6.977610136221004 -18.516462271945176
VL30-5 0.015572885853315857 0.017261476271305913
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VL30-all 0.02396144867783542 0.021699601546104813
RT-VL30-all -9.222991186865206 0.1397918338635489 -9.068672508451622 -9.407757184386957
*** budget = 40
VL40-1 0.057821030286611286 0.033944285692760444
RT-VL40-1 -9.850762287776776 3.5368638253517326 -6.028604919408093 -21.127547440243326
VL40-2 0.008670024883932266 0.006564075985848086
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VL40-3 0.007771690831411538 0.006841202881901494
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VL40-4 0.009111193270464514 0.010288614649703836
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VL40-5 0.010951750740194928 0.010457464733925044
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VL40-all 0.018865138002522906 0.01950552527407667
RT-VL40-all -9.372502426547317 0.2570298656487888 -9.109290981231885 -9.850762287776776
*** budget = 50
VL50-1 0.0502109170866979 0.03096916065057442
RT-VL50-1 -9.67846288198131 3.0218376730745926 -6.028604919408093 -19.23361015261621
VL50-2 0.0063474197307107644 0.004470522408992813
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logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00030_30_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=4_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s4/04.02_05.59.19/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00030_30_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=4_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s4/04.02_05.59.19/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00030_30_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=4_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s4/04.02_05.59.19/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 -0.08254019962267367 0.09302017220642614 -3.321832768543587e-05 -0.7337790614661913
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.036062121791394085 0.03328416043902335
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*** budget = 20
VL20-1 0.017484054393262647 0.015625711125274767
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*** budget = 30
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VL30-3 0.0008929319683431558 4.5057922705587825e-05
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VL30-5 0.05136056003791154 0.048478843482535144
RT-VL30-5 -9.975894751708946 2.183130349753534 -6.422439009986464 -22.314000500781415
VL30-all 0.02596300331070831 0.022422844637742113
RT-VL30-all -11.874758858479467 4.184412587556249 -7.969482463214697 -19.998692531052885
*** budget = 40
VL40-1 0.008951182883817766 0.009146302590844377
RT-VL40-1 -7.896246462781172 0.6334499573550547 -6.402294724917084 -9.146971772639382
VL40-2 0.006781451335834202 0.005348023145660948
RT-VL40-2 -11.251721938537264 5.279258695284175 -8.654966689358723 -28.296007982429288
VL40-3 0.0008903411262597776 5.213174186109418e-05
RT-VL40-3 -19.9982246690945 0.007538398245201506 -19.965008707700807 -20.000024138165212
VL40-4 0.046005982205325806 0.03313155154652344
RT-VL40-4 -10.458257139260542 2.684906020475449 -6.132828705951733 -25.725284100998763
VL40-5 0.041988628229414286 0.045897209613956026
RT-VL40-5 -9.557840569388128 1.2926215333078932 -6.422439009986464 -10.809683603237678
VL40-all 0.020923517156130368 0.019065843348308836
RT-VL40-all -11.83245815581232 4.232565968634462 -7.896246462781172 -19.9982246690945
*** budget = 50
VL50-1 0.007415084065024448 0.0069442608097376604
RT-VL50-1 -7.973371724542897 0.6392981527230638 -7.104675655717246 -9.216739066765227
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VL50-all 0.018374069036480704 0.0167186686486366
RT-VL50-all -11.641717689266176 4.269278791349772 -7.973371724542897 -19.997804792978
logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00022_22_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=3_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s3/04.02_05.59.23/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00022_22_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=3_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s3/04.02_05.59.23/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00022_22_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=3_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s3/04.02_05.59.23/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 -7.135636907471163 3.8095927508959204 -4.083640885016919 -38.128201168079656
LR -0.02529165252001325 0.05211034101946793 -1.6727025089480207e-06 -0.45799336112715383
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.13004952159492375 0.08819759411980166
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RT-VL10-all -7.201763816183851 0.25733726334754187 -6.834285121561591 -7.498236175285563
*** budget = 20
VL20-1 0.08151585421911113 0.06541358979197166
RT-VL20-1 -7.297142943196635 6.1796500862542025 -4.083640885016919 -35.87633652335945
VL20-2 0.028065500599166057 0.028296543488112436
RT-VL20-2 -7.136445038803504 6.196028356110815 -4.336777602821128 -38.128201168079656
VL20-3 0.018294755406296968 0.0185717859785657
RT-VL20-3 -7.618816142829184 4.3962037169254495 -5.904029798005916 -29.222098408557542
VL20-4 0.014211456697400239 0.012719801546437818
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VL20-5 0.013945490996950083 0.014325096420478281
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VL20-all 0.031206611583784894 0.02566859589549616
RT-VL20-all -7.395690285272815 0.34195628273198014 -6.989263106017053 -7.9367841955176965
*** budget = 30
VL30-1 0.057464399838916165 0.04580613193973165
RT-VL30-1 -6.55517426479312 5.584945841993172 -4.083640885016919 -35.87633652335945
VL30-2 0.019519780223525306 0.01765240917029007
RT-VL30-2 -6.075524674301064 4.229688752905092 -4.33753595931131 -27.516873621752573
VL30-3 0.012493146512612012 0.014780658140313664
RT-VL30-3 -8.032892977988697 5.2997182841575405 -5.904029798005916 -29.222098408557542
VL30-4 0.009057322503432502 0.008893152943691673
RT-VL30-4 -7.136595303513929 3.3874156799380253 -5.865053107022282 -29.996529641565765
VL30-5 0.00792771654884837 0.007267104669982979
RT-VL30-5 -6.845299033328123 0.8296419426271721 -5.436466471998958 -8.346137838648323
VL30-all 0.02129247312546687 0.01853264417304542
RT-VL30-all -6.929097250784987 0.6537349819776963 -6.075524674301064 -8.032892977988697
*** budget = 40
VL40-1 0.04382348447632135 0.03372564735665846
RT-VL40-1 -6.442515791974861 6.1665900489819885 -4.083640885016919 -35.87633652335945
VL40-2 0.014971763111720318 0.01508805161808527
RT-VL40-2 -5.740844840992704 3.3097135919453247 -4.33753595931131 -25.487100652886642
VL40-3 0.008850268192321568 0.009868951608558241
RT-VL40-3 -8.579785302150798 6.067639734833272 -5.328720105772486 -29.222098408557542
VL40-4 0.005346410627751769 0.004054015450996687
RT-VL40-4 -7.8315745033712325 5.314979891209024 -5.85969685398819 -29.996529641565765
VL40-5 0.006523673524821509 0.006539986265619437
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VL40-all 0.0159031199865873 0.014349738852786821
RT-VL40-all -7.079900474622899 1.008928914842357 -5.740844840992704 -8.579785302150798
*** budget = 50
VL50-1 0.036111606884366675 0.026587222593815223
RT-VL50-1 -5.617314353047474 3.453912672982412 -4.083640885016919 -21.829525283317253
VL50-2 0.011322064192478417 0.008612045760139367
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VL50-3 0.0064319399991801885 0.004874397719674912
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VL50-4 0.00582556933368088 0.004968713641347283
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VL50-5 0.005646971840655657 0.005738527996419485
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VL50-all 0.013067630450072362 0.011709977477647713
RT-VL50-all -7.027346048581398 1.242233169300458 -5.617314353047474 -9.03039144281046
logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00014_14_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=2_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s2/04.02_05.59.22/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00014_14_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=2_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s2/04.02_05.59.22/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00014_14_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=2_2024-04-02_05-58-41/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s2/04.02_05.59.22/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 -11.651242787584444 4.905393675250305 -5.361405331116589 -48.85257781476244
LR -0.07226754021741204 0.0735477320111402 -8.315758522448036e-06 -0.5629769984873555
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.1255928314782969 0.09975697758438094
RT-VL10-1 -11.527623638664911 3.919744162737653 -6.992371908921652 -25.364605703457542
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VL10-all 0.053839891378141234 0.039800350013720606
RT-VL10-all -11.878341017684274 3.295016018910575 -8.021856146465803 -17.91229564560333
*** budget = 20
VL20-1 0.06671524218475912 0.059014372810603254
RT-VL20-1 -11.573328001181869 4.319799038165006 -6.992371908921652 -25.364605703457542
VL20-2 0.029314541243322598 0.028334795234301574
RT-VL20-2 -11.256844295995096 2.8304989872829913 -7.945160124840739 -21.983275232204292
VL20-3 0.0017633708554570344 0.0058078911064344185
RT-VL20-3 -19.066271223592278 2.7795434756388735 -7.8337145907912475 -20.41198996565576
VL20-4 0.028090020246816387 0.024006222890282775
RT-VL20-4 -9.963354468433925 2.916125697816243 -6.987057404202085 -23.20603394925638
VL20-5 0.011936771771898218 0.011125220841750981
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VL20-all 0.027563989260450672 0.022118063129040667
RT-VL20-all -11.93051682993009 3.8076489375649096 -7.792786160447284 -19.066271223592278
*** budget = 30
VL30-1 0.046694313609250235 0.045473310692729564
RT-VL30-1 -11.454388260840524 4.412292597265296 -6.992371908921652 -25.364605703457542
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RT-VL30-2 -10.953179996273116 2.542314815185466 -7.937877641165208 -21.983275232204292
VL30-3 0.0008943068098331119 0.0005982893770293188
RT-VL30-3 -19.061488878036663 2.8191350751831874 -7.8337145907912475 -20.41198996565576
VL30-4 0.020516443215148734 0.02214666088368962
RT-VL30-4 -9.67661920485945 2.393802208093054 -6.987057404202085 -17.566442003735197
VL30-5 0.009395153119672402 0.009049098803260752
RT-VL30-5 -7.893114700807405 1.5050572110479439 -6.147333971502938 -16.262930400135776
VL30-all 0.019644614937611514 0.015428768020652735
RT-VL30-all -11.807758208163431 3.829829503047267 -7.893114700807405 -19.061488878036663
*** budget = 40
VL40-1 0.029893893819328218 0.02902953242430609
RT-VL40-1 -10.938044715158057 3.3882914169415215 -7.398353341560789 -22.294249364296594
VL40-2 0.016716302126359585 0.01534242992840671
RT-VL40-2 -10.967217110158304 2.370206646606679 -8.111632757046083 -21.983275232204292
VL40-3 0.0007883161616410195 0.00022133847692177964
RT-VL40-3 -19.28570596351772 2.354523820753586 -10.147863246028553 -20.41198996565576
VL40-4 0.014787679723002404 0.013736959269507848
RT-VL40-4 -9.946503956713272 2.398043656018854 -6.987057404202085 -15.426250167164577
VL40-5 0.005672814517553952 0.00569299270667864
RT-VL40-5 -7.535318687782973 0.683396697077466 -6.147333971502938 -9.216868268934808
VL40-all 0.013571801269577036 0.01003582019037718
RT-VL40-all -11.734558086666066 3.97677832130667 -7.535318687782973 -19.28570596351772
*** budget = 50
VL50-1 0.02744345636601407 0.026290678657230476
RT-VL50-1 -11.81861261117878 4.614452447615142 -7.398353341560789 -25.364605703457542
VL50-2 0.013828975452914923 0.013580246625925997
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VL50-3 0.0007621852218730981 0.0002383540779437381
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VL50-4 0.012710595543899124 0.016040392232115334
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VL50-all 0.012077408719959297 0.009044725258865724
RT-VL50-all -11.964070124969465 3.9194184878615808 -7.525325708124237 -19.11903172276664
logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00006_6_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=1_2024-04-02_05-58-40/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s1/04.02_05.59.19/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00006_6_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=1_2024-04-02_05-58-40/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s1/04.02_05.59.19/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/l2/run_2024-04-02_05-58-40/run_9545e_00006_6_dl_indist=2,dl_scale=1.0000,dr_cc=0.9000,seed=1_2024-04-02_05-58-40/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds1.000_zt=p_g1_s1/04.02_05.59.19/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 -8.417266107737214 3.275236263118647 -4.8634579519776455 -48.656844051099704
LR -0.03732543644854623 0.04880216929773882 -5.373645159800766e-05 -0.686502649331792
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.09308353004316304 0.06916847766302588
RT-VL10-1 -8.13673893571412 4.19122390162003 -5.190419997080271 -22.44671301386252
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VL10-all 0.0536037359889459 0.0335512649813449
RT-VL10-all -8.431795962797517 0.47539937526843634 -7.755455968643133 -9.096793630063813
*** budget = 20
VL20-1 0.056400121400817065 0.04312268641886657
RT-VL20-1 -8.087875526278319 4.549418613487347 -5.190419997080271 -22.09249042920041
VL20-2 0.015585826718894769 0.013893858914609796
RT-VL20-2 -7.692331710658033 2.3734056712190066 -5.926804984567221 -18.96981224540246
VL20-3 0.008947923918903977 0.0097199414722288
RT-VL20-3 -9.778162382073768 4.729827123199076 -6.599897299381935 -24.758436694375515
VL20-4 0.014635803247083473 0.0115964249725277
RT-VL20-4 -8.279459719039435 0.9390508600204179 -6.617983665064325 -10.392583814338629
VL20-5 0.05781515013143055 0.046832145132490066
RT-VL20-5 -9.164827702644702 4.277632952087463 -6.1373398616631745 -40.59910780779858
VL20-all 0.030676965083425968 0.021704259576199897
RT-VL20-all -8.600531408138853 0.7610485913547882 -7.692331710658033 -9.778162382073768
*** budget = 30
VL30-1 0.040814622945879994 0.03429952832199456
RT-VL30-1 -8.345987866732393 4.808302387868316 -5.542701052810608 -22.09249042920041
VL30-2 0.01091419700538676 0.00959633712499375
RT-VL30-2 -7.6537803809713285 2.4408158336484953 -6.089430185240207 -18.96981224540246
VL30-3 0.00664820526720588 0.007351808309345616
RT-VL30-3 -10.567065285100876 5.0890602659586 -6.803196315527402 -20.89494369193385
VL30-4 0.010362012739000266 0.008907118292495918
RT-VL30-4 -8.223687731043833 0.7830431388916288 -6.993472610133352 -10.178524194974614
VL30-5 0.04278763256209936 0.0426222434242053
RT-VL30-5 -8.97892138719228 4.647831147540713 -6.1373398616631745 -40.59910780779858
VL30-all 0.02230533410391445 0.01599790571667326
RT-VL30-all -8.753888530208142 0.9995982702987698 -7.6537803809713285 -10.567065285100876
*** budget = 40
VL40-1 0.03296621545351053 0.03157856763735162
RT-VL40-1 -7.363016825142002 3.4779514740537234 -5.542701052810608 -22.013602531572126
VL40-2 0.008409173365964523 0.006371030425926321
RT-VL40-2 -7.897911651717415 2.7573028407958877 -6.278501500524136 -18.96981224540246
VL40-3 0.004416581480363685 0.00490034535842602
RT-VL40-3 -11.057540194826686 5.4407128294743 -6.798834899693635 -20.000024138165212
VL40-4 0.008548787958515774 0.008780097338800797
RT-VL40-4 -8.220863269327738 0.7936103000340783 -7.247483194107463 -10.178524194974614
VL40-5 0.03254576502457563 0.03381820581169314
RT-VL40-5 -9.069342552140494 5.33964780532408 -6.1373398616631745 -40.59910780779858
VL40-all 0.01737730465658603 0.012644736145561419
RT-VL40-all -8.721734898630867 1.2924774192948976 -7.363016825142002 -11.057540194826686
*** budget = 50
VL50-1 0.029332580387254868 0.02634681914568657
RT-VL50-1 -8.02562575872094 4.624264007465461 -5.701005243737952 -22.09249042920041
VL50-2 0.007611699602592203 0.006448047095664552
RT-VL50-2 -7.589705510273779 2.2619357279921903 -6.3118564518589295 -18.920421247643052
VL50-3 0.0032515825093495008 0.003933027188052958
RT-VL50-3 -11.434418818650277 5.610443214677954 -6.8302084643066925 -20.000024138165212
VL50-4 0.006994800031515687 0.0072110608435470595
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VL50-5 0.024857385855868032 0.02695263877210386
RT-VL50-5 -9.421773824529142 5.886252537313273 -6.9493119763060855 -40.59910780779858
VL50-all 0.01440960967731606 0.010559769274329585
RT-VL50-all -8.967720476718561 1.3738998272583178 -7.589705510273779 -11.434418818650277
logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00039_39_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=5_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s5/04.01_19.36.59/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00039_39_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=5_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s5/04.01_19.36.59/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00039_39_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=5_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s5/04.01_19.36.59/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
RT -8.6882701735563 2.4914445131581613 -4.734262157913808 -34.57977064687613
LR -0.030286781839144065 0.029717751918547315 -2.569572284616406e-05 -0.38927837639551566
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.045715021775434635 0.04440462679238735
RT-VL10-1 -7.3549993615075495 1.662395136930906 -4.946122105490497 -11.923487010338633
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VL10-3 0.04906952269544622 0.0418717547451772
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VL10-4 0.026981160679485736 0.026953336383770274
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VL10-5 0.04020836410859302 0.03553595988258989
RT-VL10-5 -8.072710358846704 1.353703911601779 -5.931592849107361 -11.059601593777305
VL10-all 0.04043433559284408 0.0075305659924917055
RT-VL10-all -8.848135094597684 1.0192251133181298 -7.3549993615075495 -10.254916831535674
*** budget = 20
VL20-1 0.02335759609544333 0.022432877108266393
RT-VL20-1 -7.103780573491193 1.3413701067650634 -5.371408484805187 -10.362329605305273
VL20-2 0.022401047398015315 0.019115477652204022
RT-VL20-2 -9.569970923407743 2.348827909539338 -5.511222933362595 -17.290027907107973
VL20-3 0.02483747476180841 0.024018585422266057
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VL20-4 0.01386593280180531 0.011837100187883693
RT-VL20-4 -9.14939681191865 1.7762133870626078 -7.517386283033792 -18.58821334956634
VL20-5 0.02236771008208144 0.021061429816963465
RT-VL20-5 -7.953774351863751 1.3166518749019265 -5.999641060892829 -10.637501970779635
VL20-all 0.021365952227830762 0.0038559913433503655
RT-VL20-all -8.854325266190608 1.1971178641053486 -7.103780573491193 -10.494703670271706
*** budget = 30
VL30-1 0.014911516311357127 0.011771120389320959
RT-VL30-1 -6.825316552266255 1.002314429687726 -5.549405030013903 -9.0723391244419
VL30-2 0.016640401912660828 0.016128794922743384
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VL30-3 0.018862256495140454 0.020930359914523865
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VL30-4 0.010745717714448391 0.009761826384946526
RT-VL30-4 -9.10907794560323 1.7204291101814269 -7.517386283033792 -17.015719742726578
VL30-5 0.014989726895512564 0.013220892575262948
RT-VL30-5 -8.108822599961098 1.3121661232442696 -6.016294796171765 -10.637501970779635
VL30-all 0.015229923865823872 0.002662723095401729
RT-VL30-all -8.754914378117055 1.1738219370377776 -6.825316552266255 -10.144502203400304
*** budget = 40
VL40-1 0.01181111910385294 0.011053297103574085
RT-VL40-1 -6.9486436915109255 1.0221507000660415 -5.549405030013903 -9.171193375913552
VL40-2 0.015424447551148168 0.01725327877552754
RT-VL40-2 -9.99701388976758 2.5551638999382615 -5.631349352123286 -17.290027907107973
VL40-3 0.011004722367753966 0.009590302000148597
RT-VL40-3 -10.498085258412397 3.5624985848369604 -5.84932547501521 -20.000024138165212
VL40-4 0.008329490446542081 0.007945300635677794
RT-VL40-4 -9.177830761586591 1.7904434447733921 -7.5785592617072055 -17.015719742726578
VL40-5 0.010317862184263112 0.009620896422784584
RT-VL40-5 -8.201293640672999 1.3848165093489044 -6.4430903262305215 -10.652620113034436
VL40-all 0.011377528330712053 0.0023288609965633664
RT-VL40-all -8.964573448390098 1.2735493133476188 -6.9486436915109255 -10.498085258412397
*** budget = 50
VL50-1 0.010183484338397547 0.008546527047067666
RT-VL50-1 -6.915385592844564 1.0722668696917794 -5.549405030013903 -9.0723391244419
VL50-2 0.008796954833895414 0.009610356162016769
RT-VL50-2 -9.939877154743181 2.003631898617317 -5.631349352123286 -13.647834230282129
VL50-3 0.012067129977215778 0.016077904330915414
RT-VL50-3 -11.095108611756535 3.553732834457527 -6.142569042743953 -20.000024138165212
VL50-4 0.006316030036461314 0.006312586349641292
RT-VL50-4 -9.061553498111682 1.3655195866853316 -7.517386283033792 -11.444550630356465
VL50-5 0.00918848027158031 0.011314787527404831
RT-VL50-5 -8.012650775489707 1.2580838263190302 -6.4430903262305215 -10.637501970779635
VL50-all 0.009310415891510071 0.0018765168938908732
RT-VL50-all -9.004915126589134 1.4558154360706899 -6.915385592844564 -11.095108611756535
logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00031_31_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=4_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s4/04.01_19.36.59/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00031_31_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=4_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s4/04.01_19.36.59/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00031_31_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=4_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s4/04.01_19.36.59/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
RT -10.518557793269496 3.333442205623526 -7.274568432981133 -27.68207735032217
LR -0.054432020444836346 0.04915575759918862 -7.012517665017093e-05 -0.31089637970186845
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.03653551694182437 0.03772517816610757
RT-VL10-1 -8.293540093557056 1.952136512121926 -7.274568432981133 -22.263949332805765
VL10-2 0.048675224134562205 0.04007090724429597
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VL10-3 0.031646176265763014 0.033759372843234776
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VL10-4 0.04433294432515949 0.041296087968149556
RT-VL10-4 -10.589335807045147 1.0145932023333715 -9.139275651729099 -17.542768066509783
VL10-5 0.058890195522223035 0.06254521866369903
RT-VL10-5 -9.962936022402449 1.059669609792392 -8.02622770157676 -17.235835226739997
VL10-all 0.04401601143790642 0.0095081089778541
RT-VL10-all -10.550340515625582 1.5120642172240522 -8.293540093557056 -12.967188309212062
*** budget = 20
VL20-1 0.018602265699689688 0.01631237432976715
RT-VL20-1 -8.442960027623979 2.697442198691433 -7.274568432981133 -22.263949332805765
VL20-2 0.025703924673737593 0.02341700268811731
RT-VL20-2 -11.064337727777039 4.250978175611628 -7.82524712055014 -24.277418858879926
VL20-3 0.013304687305986891 0.01619549421698029
RT-VL20-3 -13.869878676959113 4.071868546653518 -9.916839827370204 -20.529991263040067
VL20-4 0.02444654101786225 0.025395081926404262
RT-VL20-4 -10.557388736580776 0.8465835237104944 -9.139275651729099 -12.264321062564935
VL20-5 0.025320866256179672 0.020668757701332346
RT-VL20-5 -9.857374926794872 1.0576665029234855 -8.54105328603459 -17.235835226739997
VL20-all 0.02147565699069122 0.004827238483799058
RT-VL20-all -10.758388019147157 1.7882064436732843 -8.442960027623979 -13.869878676959113
*** budget = 30
VL30-1 0.01252107873776226 0.0118104873052614
RT-VL30-1 -8.427269388512636 2.620528657905469 -7.352463506874656 -21.391622711320245
VL30-2 0.015925345417150894 0.01716336981959262
RT-VL30-2 -10.280910216653451 3.3141240237731364 -8.274927866074078 -21.90623636933507
VL30-3 0.0075716948294116794 0.009506739972288944
RT-VL30-3 -14.465926117851136 4.305428337326332 -10.00397310626699 -20.529991263040067
VL30-4 0.015762488590410403 0.015183419499033485
RT-VL30-4 -10.638575288363633 0.8573636805782924 -9.139275651729099 -12.264321062564935
VL30-5 0.01650828752979711 0.014985060154260404
RT-VL30-5 -9.801342811681595 1.155431615109195 -8.742218560628466 -17.235835226739997
VL30-all 0.01365777902090647 0.0033475234670502074
RT-VL30-all -10.722804764612489 2.016526600612117 -8.427269388512636 -14.465926117851136
*** budget = 40
VL40-1 0.011173678897577472 0.011159991393370011
RT-VL40-1 -8.211872171915603 2.1330380012590866 -7.358043528284478 -20.69230836832081
VL40-2 0.012421307175612998 0.013959867279102705
RT-VL40-2 -10.246150225308014 3.021898887047365 -8.283679935812202 -19.7935337031027
VL40-3 0.004155364610850862 0.006840640548404618
RT-VL40-3 -15.31192512688936 4.43177243477452 -9.997828008061887 -20.000024138165212
VL40-4 0.011864545542639271 0.012111634492319034
RT-VL40-4 -10.587040873973073 0.9187345430918359 -9.139275651729099 -12.158469026374501
VL40-5 0.014015283794939899 0.01337211404252229
RT-VL40-5 -9.875518806535842 1.3145874158467645 -8.988930761820809 -17.235835226739997
VL40-all 0.0107260360043241 0.0034165305812182203
RT-VL40-all -10.84650144092438 2.377049798572745 -8.211872171915603 -15.31192512688936
*** budget = 50
VL50-1 0.00743984280950755 0.008344586605516926
RT-VL50-1 -8.374533885970909 2.3401959304119724 -7.415259870630389 -20.69230836832081
VL50-2 0.010690020844748268 0.013813079825036398
RT-VL50-2 -9.674778196351467 2.3993102479266097 -8.274927866074078 -18.720203349275923
VL50-3 0.002975453575306671 0.004604117799943807
RT-VL50-3 -15.629109827354238 4.404135896250482 -10.212823275199996 -20.000024138165212
VL50-4 0.009618023102991516 0.006954463428679417
RT-VL50-4 -10.757302161544082 0.9003034653608079 -9.2483751786421 -12.158469026374501
VL50-5 0.010484343287872932 0.009692159088550114
RT-VL50-5 -9.636242160247466 0.4440538885288676 -8.988930761820809 -10.933143597719706
VL50-all 0.008241536724085388 0.002873823772799688
RT-VL50-all -10.814393246293633 2.5228726202350633 -8.374533885970909 -15.629109827354238
logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00023_23_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=3_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s3/04.01_19.36.59/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00023_23_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=3_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s3/04.01_19.36.59/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00023_23_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=3_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s3/04.01_19.36.59/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
RT -6.817676306026886 2.7277849786569957 -5.143228692245383 -51.61491956827793
LR -0.01699388661313336 0.03831099389869794 -8.93092192200129e-06 -0.5967160029887051
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.053984569973633 0.05117391069334155
RT-VL10-1 -7.017575298434495 0.8676160021097499 -5.575475317748254 -11.613959839434997
VL10-2 0.03282797847365686 0.02928880276117581
RT-VL10-2 -6.4383147075123786 0.3750984446375314 -5.356523823858 -7.73508472272102
VL10-3 0.0349290742055722 0.03503499143622457
RT-VL10-3 -6.872291028208542 3.817529356523099 -5.407521823463005 -30.2144520626357
VL10-4 0.05138819060520001 0.03980767203874202
RT-VL10-4 -6.245197528130119 0.5164050092161947 -5.245181181671049 -9.703927107028068
VL10-5 0.03616137043542298 0.03953001470908919
RT-VL10-5 -6.319308387361455 0.593692338940365 -5.143228692245383 -8.050472951668178
VL10-all 0.041858236738697006 0.008942942713039755
RT-VL10-all -6.578537389929397 0.308876358761146 -6.245197528130119 -7.017575298434495
*** budget = 20
VL20-1 0.03096297800906271 0.030920289841443273
RT-VL20-1 -7.161077749480513 0.8469432656413902 -5.575475317748254 -10.522118954923004
VL20-2 0.01751860167464948 0.01831436434326661
RT-VL20-2 -6.4411700470371525 0.3552907994372121 -5.535086915139558 -7.73508472272102
VL20-3 0.016907546483509523 0.015668758631551618
RT-VL20-3 -7.40625089389785 4.8243765362664 -5.479523701946613 -30.2144520626357
VL20-4 0.029285233097573804 0.0237030071417631
RT-VL20-4 -6.285233003760951 0.4757652145302681 -5.245181181671049 -8.321139711489662
VL20-5 0.018036901329284764 0.017944884285659518
RT-VL20-5 -6.32730433896167 0.6038506875732935 -5.143228692245383 -7.937629604617659
VL20-all 0.022542252118816056 0.006223529129312718
RT-VL20-all -6.724207206627627 0.4661286803900129 -6.285233003760951 -7.40625089389785
*** budget = 30
VL30-1 0.018083814911536152 0.016933231235045275
RT-VL30-1 -7.405647835574699 1.0302067105097341 -6.461200113181393 -11.613959839434997
VL30-2 0.012612817237626939 0.013990871010498337
RT-VL30-2 -6.479925598625617 0.309644426549632 -5.575475317748254 -7.445587543316188
VL30-3 0.012641024714577951 0.013125665954554128
RT-VL30-3 -7.623892056621901 4.841682544650473 -5.479523701946613 -27.63236248435273
VL30-4 0.018894907770378788 0.015172506661925322
RT-VL30-4 -6.2495532269311 0.4151314858686749 -5.245181181671049 -7.746605022360816
VL30-5 0.014054628222676745 0.01447352188730711
RT-VL30-5 -6.348008726446226 0.5782799022630349 -5.524035406179504 -7.492108592141535
VL30-all 0.015257438571359316 0.0027020725968713204
RT-VL30-all -6.821405488839909 0.5749872747643208 -6.2495532269311 -7.623892056621901
*** budget = 40
VL40-1 0.012024356009616956 0.012661797400073898
RT-VL40-1 -7.38255331911969 0.8529473585305322 -6.193245493162513 -10.522118954923004
VL40-2 0.008477603828010808 0.009573717588280004
RT-VL40-2 -6.4312974878389255 0.32061953972090473 -5.575475317748254 -7.445587543316188
VL40-3 0.00930567427493524 0.0075144320619760195
RT-VL40-3 -8.246204378103704 5.490034035454104 -5.479523701946613 -27.63236248435273
VL40-4 0.017296457199463218 0.014902382528595342
RT-VL40-4 -6.261504116999751 0.45134950777203814 -5.688265852710027 -7.746605022360816
VL40-5 0.00874797626456867 0.007586548632704232
RT-VL40-5 -6.290572527896181 0.5688600895393712 -5.4391485918693485 -7.336142189889826
VL40-all 0.011170413515318979 0.0033122290888198463
RT-VL40-all -6.92242636599165 0.7799192400794166 -6.261504116999751 -8.246204378103704
*** budget = 50
VL50-1 0.00997132351885957 0.012274603798575793
RT-VL50-1 -7.4918089235754985 0.9222245892477984 -6.6665743482661215 -10.522118954923004
VL50-2 0.0054435010775727545 0.005008412707797455
RT-VL50-2 -6.524590908585113 0.3815251410023963 -5.9380024928432 -7.73508472272102
VL50-3 0.007181118356138664 0.008426743281959003
RT-VL50-3 -8.738970691112437 5.9865231036423605 -5.479523701946613 -27.63236248435273
VL50-4 0.012267896952127703 0.011419623518693137
RT-VL50-4 -6.302363589557209 0.3861579230055865 -5.688265852710027 -7.746605022360816
VL50-5 0.006860636993833813 0.005871387216633448
RT-VL50-5 -6.2399566593729245 0.5477222416657465 -5.524035406179504 -7.101085813930531
VL50-all 0.0083448953797065 0.0024499264740852986
RT-VL50-all -7.059538154440636 0.9527378829135081 -6.2399566593729245 -8.738970691112437
logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00015_15_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=2_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s2/04.01_19.37.05/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00015_15_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=2_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s2/04.01_19.37.05/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00015_15_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=2_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s2/04.01_19.37.05/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 -8.2974351659234 2.543430197475442 -6.428696103459718 -28.313484186958487
LR -0.0316050310072416 0.03644944928818134 -1.4524078892907966e-05 -0.3641699009401538
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.11724222836291351 0.0702079662173153
RT-VL10-1 -8.075839300937087 0.7133103597649492 -6.642531372204187 -10.935248264339696
VL10-2 0.03269167500646029 0.033247914588165776
RT-VL10-2 -7.791308719390745 1.2397626952919547 -6.536252642838618 -21.379816405605055
VL10-3 0.018418920082674085 0.020292068229765856
RT-VL10-3 -10.448313212794345 5.21124951103347 -6.709967406493283 -20.000024138165212
VL10-4 0.03383913027827341 0.03160878889658379
RT-VL10-4 -7.785722421484052 0.8663083429690694 -7.0927292312602574 -17.86677246518405
VL10-5 0.04605454337089506 0.04473423516491727
RT-VL10-5 -7.948701345120738 0.6095999648022256 -6.752676194592526 -12.439860858155749
VL10-all 0.04964929942024327 0.034912992944542254
RT-VL10-all -8.409976999945394 1.0248592067536182 -7.785722421484052 -10.448313212794345
*** budget = 20
VL20-1 0.07550948436756334 0.04631466592803727
RT-VL20-1 -8.29075712371699 0.7568639110098542 -7.096696031889378 -10.935248264339696
VL20-2 0.015889688007740793 0.014690813303860035
RT-VL20-2 -7.734173280770063 0.5098711018433403 -6.536252642838618 -9.288687904058895
VL20-3 0.007814678588075904 0.010643936117592392
RT-VL20-3 -12.711209387605935 6.125317211770469 -6.804421547064335 -20.000024138165212
VL20-4 0.0174066762981181 0.014547041786587512
RT-VL20-4 -7.88675120709601 1.1910231267741833 -7.377179906863591 -17.86677246518405
VL20-5 0.021817646038432253 0.019619192204355835
RT-VL20-5 -7.891382192253501 0.3683236234128569 -6.752676194592526 -9.101094891564264
VL20-all 0.027687634659986075 0.02433602313014614
RT-VL20-all -8.902854638288499 1.9130911149954983 -7.734173280770063 -12.711209387605935
*** budget = 30
VL30-1 0.06285764076209956 0.042524741206104874
RT-VL30-1 -8.336644548716587 0.725059556380376 -7.403435524851648 -10.935248264339696
VL30-2 0.01118894716363391 0.010890077117780587
RT-VL30-2 -7.648781285092258 0.48179004959064786 -6.536252642838618 -8.973855765984604
VL30-3 0.005113707067004003 0.0075842766190830115
RT-VL30-3 -14.24335908999882 6.241042511645977 -6.888159500867172 -20.000024138165212
VL30-4 0.011682011300298022 0.010882628557327351
RT-VL30-4 -7.9728944509682025 1.4406248113839 -7.377179906863591 -17.86677246518405
VL30-5 0.017722180804910923 0.018417954142057786
RT-VL30-5 -7.890878983450292 0.3873913897165837 -6.752676194592526 -9.101094891564264
VL30-all 0.021712897419589282 0.02095576793872793
RT-VL30-all -9.218511671645233 2.522102515835382 -7.648781285092258 -14.24335908999882
*** budget = 40
VL40-1 0.049139567369600304 0.03629916888953826
RT-VL40-1 -8.385679173011397 0.6572763075461201 -7.519008755782995 -10.771551864256846
VL40-2 0.008201441754749404 0.00880757446705516
RT-VL40-2 -7.7426950925925535 0.4087362359378191 -6.9779169951335644 -8.973855765984604
VL40-3 0.002614831471058379 0.004561662811667641
RT-VL40-3 -15.304069104965476 6.0222751095133695 -6.988808490382536 -20.000024138165212
VL40-4 0.008507140165142157 0.009101922369934425
RT-VL40-4 -7.75340777593251 0.2752987641421875 -7.41683511146865 -8.550046293625273
VL40-5 0.012777941159043044 0.013408627151044548
RT-VL40-5 -7.9134963152833775 0.4387353767283808 -6.752676194592526 -9.101094891564264
VL40-all 0.016248184383918656 0.016759640848981947
RT-VL40-all -9.419869492357062 2.9513564158125822 -7.7426950925925535 -15.304069104965476
*** budget = 50
VL50-1 0.04417253197233402 0.03262040827519029
RT-VL50-1 -8.463217634243074 0.6557084916237133 -7.519008755782995 -10.771551864256846
VL50-2 0.007467250691623573 0.00800219210058647
RT-VL50-2 -7.739016182772803 0.45147933435135634 -6.9779169951335644 -8.973855765984604
VL50-3 0.0018956510011039706 0.0033123364901837763
RT-VL50-3 -16.267715747144468 5.704340506749102 -6.988808490382536 -20.000024138165212
VL50-4 0.007832469153239645 0.008074327153967654
RT-VL50-4 -7.753508647379869 0.29540206914053746 -7.377179906863591 -8.550046293625273
VL50-5 0.007853059860461727 0.006939764379602483
RT-VL50-5 -7.887413439764068 0.46634892848973036 -6.752676194592526 -9.101094891564264
VL50-all 0.013844192535752586 0.015331508039523822
RT-VL50-all -9.622174330260856 3.333286448440482 -7.739016182772803 -16.267715747144468
logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00007_7_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=1_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s1/04.01_19.36.58/models/ckpt_policy_T10000000.pt
0_fetchpickplacewide_v0_type1-infogsdr_ppobc-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_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: 1
ARG C_DATA: 1
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 15 Val 3 Test 7
Total data pairs: 1485, K 15, state dim 34, action dim 4, a min -0.5605148, a_max 0.5468972
Policy model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00007_7_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=1_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s1/04.01_19.36.58/models/ckpt_policy_T10000000.pt
Discr model is loaded from logs_data/30fetchv2/cgrew_dl/rr/run_2024-04-01_19-36-23/run_a6c51_00007_7_dl_indist=2,dl_scale=5.0000,dr_cc=0.9000,seed=1_2024-04-01_19-36-24/results_IL/FetchPickPlaceWide/INFOGSDR_PPOBC/0_FetchPickPlaceWide_v0_type1-INFOGSDR_PPOBC-32-32-tanh_cr10_no1_es=n_sr0011_rc0.90_reg01_ds5.000_zt=n_g1_s1/04.01_19.36.58/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 -9.101374929369983 3.394225496855052 -5.961024191742608 -35.67115782103663
LR -0.04731956031138887 0.0497502712831578 -9.54642889627877e-07 -0.4088964380999457
VL-std-all 0.0 0.0
*** budget = 10
VL10-1 0.0985578458252206 0.0734077095155789
RT-VL10-1 -8.256898908401444 2.781975039050983 -6.116930226316084 -34.38020214947006
VL10-2 0.036685132875861386 0.03462564987661766
RT-VL10-2 -8.553204202363323 3.503711009876086 -6.729767299849214 -35.67115782103663
VL10-3 0.02414215163464647 0.026028154866574577
RT-VL10-3 -10.236720560463963 4.992032586113478 -7.300801785816195 -30.912178455869356
VL10-4 0.02693251837172467 0.025407595989937373
RT-VL10-4 -8.620343670191243 1.2940001794339353 -7.226499986149726 -21.924192442449762
VL10-5 0.046295792834446986 0.052235243246391776
RT-VL10-5 -9.567243047571711 3.218024207029945 -7.382973651048225 -24.6186927169845
VL10-all 0.04652268830838002 0.02716302551704337
RT-VL10-all -9.046882077798335 0.7397590715204391 -8.256898908401444 -10.236720560463963
*** budget = 20
VL20-1 0.05904573723667902 0.044975997124312064
RT-VL20-1 -8.073394936050816 2.305817594666633 -6.129669825836606 -26.677943885362996
VL20-2 0.01706157470018266 0.01466923221030913
RT-VL20-2 -8.241963458677288 2.188499764630184 -6.729767299849214 -26.5217635258492
VL20-3 0.009844835507681364 0.011471344086732558
RT-VL20-3 -11.24571891879138 5.915925128064428 -7.34198782137853 -30.912178455869356
VL20-4 0.014759477145696908 0.013734956589736754
RT-VL20-4 -8.578639093852257 0.7282851170269325 -7.4019305044102985 -14.372363922416804
VL20-5 0.018987179214819336 0.0177746455371444
RT-VL20-5 -9.59701051603232 3.3785256674661883 -7.402526381963706 -24.6186927169845
VL20-all 0.02393976076101186 0.017816911162846165
RT-VL20-all -9.147345384680811 1.1748942984186654 -8.073394936050816 -11.24571891879138
*** budget = 30
VL30-1 0.04540174359916831 0.03674445694110225
RT-VL30-1 -8.18416824757453 2.7902194161444167 -6.129669825836606 -26.677943885362996
VL30-2 0.012911489163899297 0.011618629196899434
RT-VL30-2 -8.408340076570365 2.6357144899856686 -6.950022386878553 -26.5217635258492
VL30-3 0.005003152806562103 0.004978943924549879
RT-VL30-3 -10.939089352844126 5.145467628087543 -7.366434001656561 -27.071747339616145
VL30-4 0.0104697627001679 0.008153794090006182
RT-VL30-4 -8.601784366295863 0.8635879301466597 -7.825863854832102 -14.372363922416804
VL30-5 0.01147986536152772 0.009456756804210462
RT-VL30-5 -9.430940344759277 3.186320267336744 -7.402526381963706 -24.6186927169845
VL30-all 0.017053202726265065 0.014424985517365898
RT-VL30-all -9.112864477608833 1.0056146132301267 -8.18416824757453 -10.939089352844126
*** budget = 40
VL40-1 0.03315775284737115 0.02960773521730883
RT-VL40-1 -8.24207317012029 3.1951905871607464 -6.129669825836606 -26.677943885362996
VL40-2 0.009576686730174125 0.007896435976459434
RT-VL40-2 -8.437405116107282 3.060141250893996 -6.950022386878553 -26.5217635258492
VL40-3 0.003974486662037693 0.005104883452201965
RT-VL40-3 -11.623057687708501 5.318193568235992 -7.34198782137853 -21.358121948975903
VL40-4 0.007636480896362557 0.005725195578642492
RT-VL40-4 -8.460001993528309 0.23483099560206852 -7.825863854832102 -8.986731328638484
VL40-5 0.009674875226406835 0.008487939643451525
RT-VL40-5 -9.856817747523058 3.84119912249905 -7.434208724409738 -24.6186927169845
VL40-all 0.01280405647247047 0.010384081925889688
RT-VL40-all -9.323871142997488 1.286286875390698 -8.24207317012029 -11.623057687708501
*** budget = 50
VL50-1 0.029979804211901804 0.026324428255250196
RT-VL50-1 -8.335771419917062 3.524214935156823 -6.129669825836606 -26.677943885362996
VL50-2 0.007279346390898622 0.0069562455957540535
RT-VL50-2 -8.599476384486895 3.3635491310172974 -7.0232349627805934 -26.5217635258492
VL50-3 0.0029243308128148312 0.0037798753754428585
RT-VL50-3 -11.802077142655412 5.473139192164439 -7.4121403548017355 -21.358121948975903
VL50-4 0.0066522847525383256 0.005746700716178679
RT-VL50-4 -8.473229061778005 0.24669063960906432 -7.825863854832102 -8.986731328638484
VL50-5 0.0077494672856448735 0.006210165340423826
RT-VL50-5 -9.847643807561523 3.8598630543795367 -7.434208724409738 -24.6186927169845
VL50-all 0.01091704669075969 0.00968221925556771
RT-VL50-all -9.411639563279781 1.311640734510898 -8.335771419917062 -11.802077142655412