Adaptation of Quadruped Robot Locomotion with Meta-Learning
- URL: http://arxiv.org/abs/2107.03741v1
- Date: Thu, 8 Jul 2021 10:37:18 GMT
- Title: Adaptation of Quadruped Robot Locomotion with Meta-Learning
- Authors: Arsen Kuzhamuratov, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky
- Abstract summary: We demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks.
The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.
- Score: 64.71260357476602
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Animals have remarkable abilities to adapt locomotion to different terrains
and tasks. However, robots trained by means of reinforcement learning are
typically able to solve only a single task and a transferred policy is usually
inferior to that trained from scratch. In this work, we demonstrate that
meta-reinforcement learning can be used to successfully train a robot capable
to solve a wide range of locomotion tasks. The performance of the meta-trained
robot is similar to that of a robot that is trained on a single task.
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