DrQ-v2: Improved Data-Augmented RL Agent


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 challening 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:

  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:},


Install dependencies:

conda env create -f conda_env.yml
conda activate drqv2

Train the agent:

python train.py task=quadruped_walk

Monitor results:

tensorboard --logdir exp_local