This is an original PyTorch implementation of DrQ-v2 from
[Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning] by
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto.
DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ, an actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements including:
- Switch the base RL learner from SAC to DDPG.
- Incorporate n-step returns to estimate TD error.
- Introduce a decaying schedule for exploration noise.
- Make implementation 3.5 times faster.
- Find better hyper-parameters.
These changes allow us to significantly improve sample efficiency and wall-clock training time on a set of challenging tasks from the DeepMind Control Suite compared to prior methods. Furthermore, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL.
If you use this repo in your research, please consider citing the paper as follows:
@article{yarats2021drqv2,
title={Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning},
author={Denis Yarats and Rob Fergus and Alessandro Lazaric and Lerrel Pinto},
journal={arXiv preprint arXiv:2107.09645},
year={2021}
}
Please also cite our original paper:
@inproceedings{yarats2021image,
title={Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels},
author={Denis Yarats and Ilya Kostrikov and Rob Fergus},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=GY6-6sTvGaf}
}
Install MuJoCo if it is not already the case:
- Obtain a license on the MuJoCo website.
- Download MuJoCo binaries here.
- Unzip the downloaded archive into
~/.mujoco/mujoco200and place your license key filemjkey.txtat~/.mujoco. - Use the env variables
MUJOCO_PY_MJKEY_PATHandMUJOCO_PY_MUJOCO_PATHto specify the MuJoCo license key path and the MuJoCo directory path. - Append the MuJoCo subdirectory bin path into the env variable
LD_LIBRARY_PATH.
Install the following libraries:
sudo apt update
sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3Install dependencies:
conda env create -f conda_env.yml
conda activate drqv2Train the agent:
python train.py task=quadruped_walkMonitor results:
tensorboard --logdir exp_localThe majority of DrQ-v2 is licensed under the MIT license, however portions of the project are available under separate license terms: DeepMind is licensed under the Apache 2.0 license.
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