単眼カメラ画像入力に基づく自律走行モデル獲得のためのシミュレーション環境最適化

抄録

This study introduces a training system of action models for mobile robot navigation based on reinforcement learning with monocular camera input. The authors have developed a synthesis system that includes automatically building simulation environments and training of models. The purpose of this paper is to obtain a general-purpose action model by quantitatively verifying the effects of the proposed system. The simulation result shows that the trained model with the proposed system provides stable navigation performance.