# Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic

## TL;DR

This paper introduces a reinforcement learning method for autonomous vehicle merging in dense traffic that models driver cooperation levels, leading to fewer deadlocks compared to traditional planning methods.

## Contribution

It proposes a novel reinforcement learning approach that maintains a belief over driver cooperation levels to improve decision making in dense traffic merging scenarios.

## Key findings

- Reduces deadlocks in dense merging scenarios.
- Learns to interact effectively with drivers of varying cooperation levels.
- Outperforms online planning methods in dense traffic maneuvers.

## Abstract

Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.