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README.md

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+
+# V-D4RL
+
+<p align="center">
+  <img src="figs/envs.png" />
+</p>
+
+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:
+
+**_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.
+
+<p align="center">
+  <a href=https://arxiv.org/abs/2206.04779>View on arXiv</a>
+</p>
+
+## Benchmarks
+The V-D4RL datasets can be found in this repository under `vd4rl`. **These must be downloaded before running the code.** Assuming the data is stored under `vd4rl_data`, the file structure is:
+
+```
+vd4rl_data
+└───main
+│   └───walker_walk
+│   │   └───random
+│   │   │   └───64px
+│   │   │   └───84px
+│   │   └───medium_replay
+│   │   │   ...
+│   └───cheetah_run
+│   │   ...
+│   └───humanoid_walk
+│   │   ...
+└───distracting
+│   ...
+└───multitask
+│   ...
+```
+
+## Baselines
+
+### Environment Setup
+Requirements are presented in conda environment files named `conda_env.yml` within each folder. The command to create the environment is:
+```
+conda env create -f conda_env.yml
+```
+
+Alternatively, dockerfiles are located under `dockerfiles`, replace `<<USER_ID>>` in the files with your own user ID from the command `id -u`.
+
+### V-D4RL Main Evaluation
+Example run commands are given below, given an environment type and dataset identifier:
+
+```
+ENVNAME=walker_walk # choice in ['walker_walk', 'cheetah_run', 'humanoid_walk']
+TYPE=random # choice in ['random', 'medium_replay', 'medium', 'medium_expert', 'expert']
+```
+
+#### Offline DV2 
+```
+python offlinedv2/train_offline.py --configs dmc_vision --task dmc_${ENVNAME} --offline_dir vd4rl_data/main/${ENV_NAME}/${TYPE}/64px --offline_penalty_type meandis --offline_lmbd_cons 10 --seed 0
+```
+
+#### DrQ+BC
+```
+python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px nstep=3 seed=0
+```
+
+#### DrQ+CQL
+```
+python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px algo=cql cql_importance_sample=false min_q_weight=10 seed=0
+```
+
+#### BC
+```
+python drqbc/train.py task_name=offline_${ENVNAME}_${TYPE} offline_dir=vd4rl_data/main/${ENV_NAME}/${TYPE}/84px algo=bc seed=0
+```
+
+### Distracted and Multitask Experiments
+To run the distracted and multitask experiments, it suffices to change the offline directory passed to the commands above.
+
+## Note on data collection and format
+We follow the image sizes and dataset format of each algorithm's native codebase.
+The means that Offline DV2 uses `*.npz` files with 64px images to store the offline data, whereas DrQ+BC uses `*.hdf5` with 84px images.
+
+The data collection procedure is detailed in Appendix B of our paper, and we provide conversion scripts in `conversion_scripts`.
+For the original SAC policies to generate the data see [here](https://github.com/philipjball/SAC_PyTorch/blob/dmc_branch/train_agent.py).
+See [here](https://github.com/philipjball/SAC_PyTorch/blob/dmc_branch/gather_offline_data.py) for distracted/multitask variants.
+We used `seed=0` for all data generation.
+
+## Acknowledgements
+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:
+- [D4RL](https://github.com/rail-berkeley/d4rl)
+- [DMControl](https://github.com/deepmind/dm_control)
+- [DreamerV2](https://github.com/danijar/dreamerv2)
+- [DrQ-v2](https://github.com/facebookresearch/drqv2)
+- [LOMPO](https://github.com/rmrafailov/LOMPO)
+
+## Contact
+Please contact [Cong Lu](mailto:cong.lu@stats.ox.ac.uk) or [Philip Ball](mailto:ball@robots.ox.ac.uk) for any queries.