Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

• URL: http://arxiv.org/abs/2107.09645v1
• Date: Tue, 20 Jul 2021 17:29:13 GMT
• Title: Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning
• Authors: Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto
• Abstract summary: We present DrQ-v2, a model-free reinforcement learning algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations.
• Score: 114.35801511501639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, we publicly release DrQ-v2's implementation to provide RL practitioners with a strong and computationally efficient baseline.

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