モデル予測シールディングによる安全なマルチエージェント強化学習
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding
学習済み方策をできるだけ使いながら、安全が保証できない場合にバックアップ方策へ切り替えることで、マルチエージェント環境での安全性を理論的に保証する手法を提案した。
著者: Wenbo Zhang, Osbert Bastani, Vijay Kumar
分類: eess.SY, cs.AI, cs.MA, cs.RO, cs.SY
原文アブストラクト
Reinforcement learning is a promising approach to learning control policies for performing complex multi-agent robotics tasks. However, a policy learned in simulation often fails to guarantee even simple safety properties such as obstacle avoidance. To ensure safety, we propose multi-agent model predictive shielding (MAMPS), an algorithm that provably guarantees safety for an arbitrary learned policy. In particular, it operates by using the learned policy as often as possible, but instead uses a backup policy in cases where it cannot guarantee the safety of the learned policy. Using a multi-agent simulation environment, we show how MAMPS can achieve good performance while ensuring safety.
