移動ロボットの自律走行のための深層強化学習で与える報酬による走行経路への影響

抄録

We developed an autonomous mobile robot system based on behaviors acquired by deep reinforcement learning. Navigation performances including traveling trajectories are affected by the design of state, action and reward in deep reinforcement learning. This paper focuses on rewards given in training of action policies on the simulator. For example, negative rewards are given to the situations that the robot approaches to obstacles closely. Then, the robot has a tendency to run far from obstacles. In the paper, robot navigation experiments in a real world were performed. Differences of the trajectories according to several rewards are discussed.