Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
This work addresses a domain-specific challenge for autonomous vehicles, with incremental improvements in training robustness.
The paper tackled the problem of autonomous vehicle merging in dense traffic by combining reinforcement learning with game theory and a level-k behavior curriculum, resulting in more efficient policies than traditional methods.
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.