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train_all_neurips2025_simultaneous.sh
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executable file
·44 lines (38 loc) · 1.99 KB
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#!/bin/bash
SESSION_NAME="targetnet_train_rss2025_final_exp_simultaneous_scheduler_no_eps"
# Check if the session already exists
if tmux has-session -t $SESSION_NAME 2>/dev/null; then
echo "Session $SESSION_NAME already exists. Attaching to it."
tmux attach-session -t $SESSION_NAME
else
# Create a new session and name it
tmux new-session -d -s $SESSION_NAME
# declare -a MODELS=("hgt" "hgt_resnet" "hgt_edge" "hgt_edge_resnet")
declare -a MODELS=("hgt")
# declare -a MODELS=("hgt_resnet" "hgt_edge" "hgt_edge_resnet") # rerun for final version
declare -a SEEDS=(10 11 12)
declare -a CRITIC_MODE=("")
# 10 Robot 20 Task 200 Problems
for model in "${MODELS[@]}"
do
name="${model}${critic_choice}"
tmux new-window -n $name
tmux split-window -h
tmux split-window -h
tmux select-layout even-horizontal
for seed in "${SEEDS[@]}"
do
index=$(($seed - 10 + 1))
tmux send-keys -t $name.$index 'export PYTHONHASHSEED=0' C-m # ensures full reprodibility and internal consistency
tmux send-keys -t $name.$index "index=${index}" C-m
tmux send-keys -t $name.$index "seed=${seed}" C-m
tmux send-keys -t $name.$index 'echo "Hello from pane ${index}"' C-m
tmux send-keys -t $name.$index 'conda activate dreamcatcher' C-m
tmux send-keys -t $name.$index "model=${model}" C-m
tmux send-keys -t $name.$index "critic=${critic}" C-m
tmux send-keys -t $name.$index 'python training/sac_wip_simultaneous.py --env_location data/problem_set_r10_t20_s0_f10_w25_euc_2000_uni --end_problem 200 --env_id "paper neurips25 10r20t0s10f25w200p ${model} simultaneous reward-greedy-final1" --num_heads 1 --num_layers 4 --no-partition_learning --graph_mode ${model} --baseline_boosting --seed ${seed} --adaptive_temperature --track --gamma 0.95' C-m
done
done
# Attach to the created session
tmux attach-session -t $SESSION_NAME
fi