1A1-J04 強化学習を用いた大車輪ロボットの行動獲得と学習プロセスについて(進化・学習とロボティクス)

Abstract

In our laboratory, we have succeeded in acquiring forward actions to various robot systems using Reinforcement Learning. We have also succeeded in acquiring a giant swing motion as dynamic task by devising its rewards. Then, the purpose of this study is to investigate the effect of probabilistic behavior which gives to the giant swing motion acquisition. Although the giant swing robot has a continuous dynamic motion such as its angle and angler velocity, its state of the motion must be divided into 216 states in order to apply the reinforcement learning. For this reason, this robot shows probabilistic behaviors. This study investigated the relationship between the performance of learning knowledge and rotation count of the learning process. Consequently, it became clear that the performance of the knowledge is deteriorated when the ratio of knowledge use of the learning process is large.