The paper is located here.
To run experiments, first build the docker container by running build.sh from inside the docker folder.
The following launches a docker container on GPU $GPU_ID (set to 0 if you want to run on the first gpu) with the parameters specified (shown as ...):
bash run.sh $GPU_ID python3 src/main.py with ... repeat_id=0
To run OPIQ on the Maze:
bash run.sh 0 python3 src/main.py with t_max=1050000 env=Maze-v0 agent=2conv batch_size=64 epsilon_time_length=50000 epsilon_finish=0.01 test_episodes=1 testing_interval=20000 log_interval=10000 count_rewards=True count_beta=0.1 count_size=128 recompute_count_rewards=True n_step=3 buffer_size=250000 double_q=False count_state_only_rewards=False save=True reward_clipping=False target_update_interval=1000 gamma=0.99 episode_limit=250 name=opiq action_selector=optimistic_action optim_m=2 optim_action_tau=100 optim_bootstrap=True optim_bootstrap_tau=0.01 label=maze repeat_id=1
Montezuma:
bash run.sh 0 python3 src/main.py with t_max=13050000 env=Montezuma-v0 past_frames_input=4 agent=atari batch_size=32 update_freq=4 epsilon_time_length=1000000 epsilon_finish=0.01 test_epsilon=0.01 test_episodes=5 testing_interval=100000 log_interval=50000 count_rewards=True count_beta=0.1 atari_count=True recompute_count_rewards=True n_step=3 buffer_size=1000000 double_q=False count_state_only_rewards=False save=True reward_clipping=True target_update_interval=8000 gamma=0.99 mmc=True name=OPIQ action_selector=optimistic_action optim_m=2 optim_action_tau=0.1 mmc_beta=0.01 optim_bootstrap=True optim_bootstrap_tau=0.01 label=montezuma_opiq
The config file default.yaml in src/config contains all the possible flags/hyperparameters.
Sorry about the lack of documentation and general messiness of the code, if you have any questions don't hesitate to raise an issue or send me an email: [email protected].
Bibtex:
@inproceedings{rashid2019optimistic,
title={Optimistic Exploration even with a Pessimistic Initialisation},
author={Rashid, Tabish and Peng, Bei and Boehmer, Wendelin and Whiteson, Shimon},
booktitle={International Conference on Learning Representations},
year={2019}
}