[arxiv], Accepted to ICLR2022 (Spotlight)
If you use this codebase for your research, please cite the paper:
@inproceedings{furuta2021generalized,
title={Generalized Decision Transformer for Offline Hindsight Information Matching},
author={Hiroki Furuta and Yutaka Matsuo and Shixiang Shane Gu},
booktitle={International Conference on Learning Representations},
year={2022}
}
Experiments require MuJoCo. Follow the instructions in the mujoco-py repo to install. Then, dependencies can be installed with the following command:
conda env create -f conda_env.yml
Datasets are stored in the data directory.
Install the D4RL repo, following the instructions there.
Then, run the following script in order to download the datasets and save them in our format:
python download_d4rl_datasets.py
Run train_cdt.py to train Categorical DT:
python train_cdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --condition 'reward' --save_model True
python train_cdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --condition 'xvel' --save_model True
Run eval_cdt.py to eval CDT using saved weights:
python eval_cdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --condition 'reward' --save_rollout True
python eval_cdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --condition 'xvel' --save_rollout True
For Bi-directional DT, run train_bdt.py & eval_bdtf.py
python train_bdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --z_dim 16 --save_model True
python eval_bdt.py --env halfcheetah --dataset medium-expert --gpu 0 --seed 0 --dist_dim 30 --n_bins 31 --z_dim 16 --save_rollout True
This repository is developed on top of original Decision Transformer.
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