Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic

• URL: http://arxiv.org/abs/2005.11895v1
• Date: Mon, 25 May 2020 02:57:19 GMT
• Title: Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
• Authors: Maxime Bouton, Alireza Nakhaei, David Isele, Kikuo Fujimura, and Mykel J. Kochenderfer
• Abstract summary: Maneuvering in dense traffic is a challenging task for autonomous vehicles. We propose a combination of reinforcement learning and game theory to learn merging behaviors.
• Score: 41.46201285202203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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