A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning

2022-08-16 · Laura Smith, Ilya Kostrikov, Sergey Levine

Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefficiency, deep RL applications have primarily focused on simulated environments. In this work, we demonstrate that the recent advancements in machine learning algorithms and libraries combined with a carefully tuned robot controller lead to learning quadruped locomotion in only 20 minutes in the real world. We evaluate our approach on several indoor and outdoor terrains which are known to be challenging for classical model-based controllers. We observe the robot to be able to learn walking gait consistently on all of these terrains. Finally, we evaluate our design decisions in a simulated environment.

📄 PDF Abstract BibTeX arXiv:2208.07860

Code (1)

ikostrikov/walk_in_the_park 공식 구현

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning (RL)

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