ロボットネットワークにおける自己位置の共有を利用した深層強化学習による移動ロボットの自律走行モデルの獲得

抄録

The purpose of this study is to develop an autonomous navigation system for mobile robots using deep reinforcement learning. Especially, this study assumes that multiple mobile robots are connected to the network and the positions of networked robots are shared each other. In this paper, deep reinforcement learning using the positions of the other robots is performed. Then, action models that can avoid the other robots and reach the destinations are obtained. Unity is used as a simulation environment for learning and an agent and the other mobile robots are placed in the environments. As the training result, the action model that the agent can arrive at goals while avoiding the other robots are obtained.