相対情報のみを用いたマルチエージェント強化学習による分散フォーメーションと障害物回避
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
各エージェントが局所的・相対的な情報だけで分散的に判断し、フォーメーション形成と障害物回避を同時に達成するMARL手法を提案。切断時にも素早く再編成でき、シミュレーションと実機で有効性を検証した。
著者: Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu, Jian Wang, Yu Wang, Yuan Shen
分類: eess.SY, cs.AI, cs.LG, cs.MA, cs.RO, cs.SY
原文アブストラクト
Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuverability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively. Agent in the multi-agent system will reorganize themselves into a new topology quickly in case that any of them is disconnected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.
