反復推論を用いた強化学習による密集交通での合流
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
ゲーム理論のレベルk思考と強化学習を組み合わせ、多様な他車挙動に頑健な密集交通での合流ポリシーを学習させた研究。
著者: Maxime Bouton, Alireza Nakhaei, David Isele, Kikuo Fujimura, Mykel J. Kochenderfer
分類: cs.AI, cs.RO
原文アブストラクト
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the agent must achieve the maneuver within a limited time and distance. In this work, we propose a combination of reinforcement learning and game theory to learn merging behaviors. We design a training curriculum for a reinforcement learning agent using the concept of level-$k$ behavior. This approach exposes the agent to a broad variety of behaviors during training, which promotes learning policies that are robust to model discrepancies. We show that our approach learns more efficient policies than traditional training methods.
