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README.md

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Bayesian Surprise

Repo for environments, gym wrappers, and scripts for the SMiRL project.

Requirements:

  • For distributing experiments.

doodad: https://github.com/montrealrobotics/doodad

  • RL library

rlkit: https://github.com/Neo-X/rlkit/tree/surprise

Build Instruction

conda create --name smirl_code python=3.7 pip
conda activate smirl_code
pip install -r requirements.txt
pip install -e ./
cd ../
git clone [email protected]:montrealrobotics/doodad.git
cd doodad
pip install -e ./
cd ../smirl_code

Then you will need copy the config.py file locally to launchers.config.py and update the paths in the file. You need to update BASE_CODE_DIR to the location you have saved SMiRL_Code. Also update LOCAL_LOG_DIR to the location you would like the logging data to be saved on your computer. You can look at the doodad for more details on this configuration.

Commands:

A basic examples.

python3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --run_mode=local --exp_name=test_smirl

python3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --run_mode=local --exp_name=test_smirl --training_processor_type=gpu

With docker locally

python3 scripts/dqn_smirl.py --config=configs/tetris_SMiRL.json --exp-name=test --run_mode=local_docker

###Run Vizdoom SMiRL experiments

python3 scripts/dqn_smirl.py --config=configs/VizDoom_TakeCover_Small.json --exp_name=vizdoom_small_test --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

python3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small.json --exp_name=vizdoom_DTL_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

python3 scripts/dqn_smirl.py --config=configs/VizDoom_DefendTheLine_Small_Bonus.json --exp_name=vizdoom_DTL_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

Run Atari Experiments

python3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL.json --exp_name=Atari_Carnival__small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

python3 scripts/dqn_smirl.py --config=configs/Carnival_Small_SMiRL_Bonus.json --exp_name=Atari_Carnival_small_smirl_bonus --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

python3 scripts/dqn_smirl.py --config=configs/IceHockey_Small_SMiRL.json --exp_name=Atari_IceHockey_small_smirl --run_mode=ssh --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json

python3 scripts/dqn_smirl.py --config=configs/RiverRaid_Small_SMiRL.json --exp_name=Atari_RiverRaid_small_smirl --run_mode=ssh --ssh_host=newton1 --random_seeds=1 --meta_sim_threads=4 --log_comet=true --training_processor_type=gpu --tuningConfig=configs/GPU_indexes.json