Skip to content

Latest commit

 

History

History
59 lines (36 loc) · 2.41 KB

README.md

File metadata and controls

59 lines (36 loc) · 2.41 KB
Raw

Towards Robust Offline RL under Diverse Data Corruption

This repo contains the official implemented Robust IQL (RIQL) algorithm for the ICLR 2024 spotlight paper (⭐ top 5%), "Towards Robust Offline Reinforcement Learning under Diverse Data Corruption". This code is implemented based on the open-sourced CORL library.

Getting started

Install torch>=1.7.1, gym, mujoco_py, d4rl, pyrallis, wandb, tqdm.

Under Random Data Corruption

Run RIQL with random observation corruption:

CUDA_VISIBLE_DEVICES=${gpu} python RIQL.py --corruption_mode random  --corrupt_obs --corruption_range ${corruption_range} --corruption_rate ${corruption_rate}  --env_name ${env_name} --seed ${seed}

'env_name' can be 'halfcheetah-medium-replay-v2', 'walker2d-medium-replay-v2', 'hopper-medium-replay-v2', ....

'corruption_range' and 'corruption_rate' are set to 1.0 and 0.3 by default.

Replace '--corrupt_obs' with '--corrupt_reward', '--corrupt_acts', and '--corrupt_dynamics' to enforce corruption on rewards, actions, and dynamics.

Under Adversarial Data Corruption

Run RIQL with adversarial observation corruption:

CUDA_VISIBLE_DEVICES=${gpu} python RIQL.py --corruption_mode adversarial --corruption_obs --corruption_range ${corruption_range} --corruption_rate ${corruption_rate}  --env_name ${env_name} --seed ${seed}

The adversarial attacks on obs, actions, and next-obs require performing gradient-based attack and will save the corrupted data. After saving the corrupted data, we will load these data for later training.

Clean Data

To run the algorithm with a clean dataset, you can run the following command without specifying the corruption-related parameters

CUDA_VISIBLE_DEVICES=${gpu} python RIQL.py  --env_name ${env_name} --seed ${seed}

Baselines

You can replace the RIQL.py with other baselines, such as IQL.py, CQL.py, EDAC.py, and MSG.py, to run IQL, CQL, EDAC, and MSG.

Citation

If you find our work helpful for your research, please cite:

@inproceedings{yang2023towards,
  title={Towards Robust Offline Reinforcement Learning under Diverse Data Corruption},
  author={Yang, Rui and Zhong, Han and Xu, Jiawei and Zhang, Amy and Zhang, Chongjie and Han, Lei and Zhang, Tong},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024},
  url={https://openreview.net/forum?id=5hAMmCU0bK}
}