[2Yin5] インタラクティブ2

[2Yin5-09] Terrain Parameterization in Curriculum Reinforcement Learning for Legged Locomotion Robots

〇Shiho Sasaki¹, Wataru Okamoto¹, Kouhei Osato¹, Kazuhiko Kawamoto² (1.Graduate School of Science and Engineering, Chiba University, 2.Graduate School of Engineering, Chiba University)

Keywords:reinforcement learning

In robot control using reinforcement learning, it is becoming common to acquire polices in a simulation environment and then apply them to a real environment. Since there is a gap between their environments, several methods have been proposed for bridging the gap by training robots in various simulation environments. In this work, we propose a curriculum reinforcement learning method for robots that can walk in various terrains. For the curriculum learning, the terrain in the simulation environment is represented by an Ising model and its interaction parameter is used to determine the complexity of the terrain shape. From the nature of the Ising model, the terrain becomes flat when the interaction parameter is large and uneven when it is small. The evaluation experiments show the effectiveness of the terrain parameterization for curriculum reinforcement learning.