Meta AI, University of Texas at Austin

ICLR 2024, https://arxiv.org/abs/2305.14550

Download the humanoid offline RL data from here: https://dl.fbaipublicfiles.com/prajj/rl_paradigm/humanoid_offline_rl_data.tar.gz

Each directory contains a script directory within it. The script within script directory was used to run experiments for this study. All the scripts are highly configurable. Change the parameter according to your needs.

$ cd atari
$ sbatch scripts/train_atari.sh

For DT and BC, use

$ cd gym
$ sbatch scripts/train_gym.sh

For CQL, use

$ cd gym
$ sbatch scripts/train_cql.sh

$ cd robomimic
$ sbatch scripts/train_default.sh

These scripts will create five directories named 1, 2, etc. depending on the number of seed provided. Each of these directory will contain a result.json file. Each directory is accompanied by read_data.py or read_data_cql.py. After the results are dumped, these scripts can be used to get the average mean and std dev from all the runs.

python3 read_data.py --json_file_path $FILE_TO_RESULT_DIR