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--- |
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license: mit |
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thumbnail: "https://github.com/conglu1997/v-d4rl/raw/main/figs/envs.png" |
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tags: |
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- Reinforcement Learning |
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- Offline Reinforcement Learning |
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- Reinforcement Learning from Pixels |
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- DreamerV2 |
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- DrQ+BC |
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datasets: |
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- V-D4RL |
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--- |
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# V-D4RL |
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V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the **`dm_control`** suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting. For further details, please see the paper: |
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**_Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations_**; Cong Lu*, Philip J. Ball*, Tim G. J. Rudner, Jack Parker-Holder, Michael A. Osborne, Yee Whye Teh. |
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<p align="center"> |
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<a href=https://arxiv.org/abs/2206.04779>View on arXiv</a> |
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</p> |
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## Benchmarks |
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The V-D4RL datasets can be found in this repository under `vd4rl`. They may also be found on [Google Drive](https://drive.google.com/drive/folders/15HpW6nlJexJP5A4ygGk-1plqt9XdcWGI?usp=sharing). **These must be downloaded before running the code.** Assuming the data is stored under `vd4rl_data`, the file structure is: |
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``` |
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vd4rl_data |
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└───main |
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│ └───walker_walk |
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│ │ └───random |
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│ │ │ └───64px |
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│ │ │ └───84px |
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│ │ └───medium_replay |
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│ │ │ ... |
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│ └───cheetah_run |
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│ │ ... |
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│ └───humanoid_walk |
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│ │ ... |
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└───distracting |
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│ ... |
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└───multitask |
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│ ... |
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``` |
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## Baselines |
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### Environment Setup |
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Requirements are presented in conda environment files named `conda_env.yml` within each folder. The command to create the environment is: |
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``` |
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conda env create -f conda_env.yml |
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``` |
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Alternatively, dockerfiles are located under `dockerfiles`, replace `<<USER_ID>>` in the files with your own user ID from the command `id -u`. |
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### V-D4RL Main Evaluation |
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Example run commands are given below, given an environment type and dataset identifier: |
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``` |
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ENVNAME=walker_walk # choice in ['walker_walk', 'cheetah_run', 'humanoid_walk'] |
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TYPE=random # choice in ['random', 'medium_replay', 'medium', 'medium_expert', 'expert'] |
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``` |
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#### Offline DV2 |
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``` |
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python offlinedv2/train_offline.py --configs dmc_vision --task dmc_${ENVNAME} --offline_dir vd4rl_data/main/${ENV_NAME}/${TYPE}/64px --offline_penalty_type meandis --offline_lmbd_cons 10 --seed 0 |
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``` |
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#### DrQ+BC |
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``` |
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python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px nstep=3 seed=0 |
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``` |
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#### DrQ+CQL |
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``` |
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python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px algo=cql cql_importance_sample=false min_q_weight=10 seed=0 |
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``` |
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#### BC |
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``` |
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python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px algo=bc seed=0 |
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``` |
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### Distracted and Multitask Experiments |
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To run the distracted and multitask experiments, it suffices to change the offline directory passed to the commands above. |
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## Note on data collection and format |
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We follow the image sizes and dataset format of each algorithm's native codebase. |
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The means that Offline DV2 uses `*.npz` files with 64px images to store the offline data, whereas DrQ+BC uses `*.hdf5` with 84px images. |
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The data collection procedure is detailed in Appendix B of our paper, and we provide conversion scripts in `conversion_scripts`. |
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For the original SAC policies to generate the data see [here](https://github.com/philipjball/SAC_PyTorch/blob/dmc_branch/train_agent.py). |
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See [here](https://github.com/philipjball/SAC_PyTorch/blob/dmc_branch/gather_offline_data.py) for distracted/multitask variants. |
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We used `seed=0` for all data generation. |
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## Acknowledgements |
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V-D4RL builds upon many works and open-source codebases in both offline reinforcement learning and online pixel-based continuous control. We would like to particularly thank the authors of: |
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- [D4RL](https://github.com/rail-berkeley/d4rl) |
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- [DMControl](https://github.com/deepmind/dm_control) |
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- [DreamerV2](https://github.com/danijar/dreamerv2) |
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- [DrQ-v2](https://github.com/facebookresearch/drqv2) |
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- [LOMPO](https://github.com/rmrafailov/LOMPO) |
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## Contact |
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Please contact [Cong Lu](mailto:cong.lu@stats.ox.ac.uk) or [Philip Ball](mailto:ball@robots.ox.ac.uk) for any queries. We welcome any suggestions or contributions! |
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