深層強化学習によるマルチロボットナビゲーションのための堅牢な制御・ナビゲーション方策の獲得
Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning
深層強化学習を用いて、生のセンサデータから速度指令への分散型エンドツーエンド方策を学習し、複数ロボットの協調ナビゲーションを実現した。
著者: Christian Jestel, Hartmut Surmann, Jonas Stenzel, Oliver Urbann, Marius Brehler
分類: cs.RO
原文アブストラクト
Multi-robot navigation is a challenging task in which multiple robots must be coordinated simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) to learn a decentralized end-to-end policy which maps raw sensor data to the command velocities of the agent. In order to enable the policy to generalize, the training is performed in different environments and scenarios. The learned policy is tested and evaluated in common multi-robot scenarios like switching a place, an intersection and a bottleneck situation. This policy allows the agent to recover from dead ends and to navigate through complex environments.
