深層強化学習による移動ロボットの自律走行のための行動方策自動獲得システムの提案

抄録

Autonomous mobile robot navigation system are fundamental and important in robot technology. In recent years, researches about autonomous navigation system based on reinforcement learning have increased. It is expected that autonomous navigation system based on deep reinforcement learning is achieved without grid map prepared in advance. However, in autonomous navigation based on deep reinforcement learning, the difficulties of action and reward design are mentioned. In this study, we aim at an automatic action policy acquisition system that designs them based on clustering of velocities and generative adversarial imitative learning from behaviors of expert robots. In this paper, we describe system abstract and develop system. Furthermore, we perform several experiments to examine the validity of the system.