|
--- |
|
license: mit |
|
thumbnail: "https://github.com/conglu1997/v-d4rl/raw/main/figs/envs.png" |
|
tags: |
|
- Reinforcement Learning |
|
- Offline Reinforcement Learning |
|
- Reinforcement Learning from Pixels |
|
- DreamerV2 |
|
- DrQ+BC |
|
datasets: |
|
- V-D4RL |
|
|
|
--- |
|
|
|
# V-D4RL |
|
|
|
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. |
|
|
