Many scenarios in mobility and traffic involve multiple different agents that
need to cooperate to find a joint solution. Recent advances in behavioral
planning use Reinforcement Learning to find effective and performant behavior
strategies. However, as autonomous vehicles and vehicle-to-X communications
become more mature, solutions that only utilize single, independent agents
leave potential performance gains on the road. Multi-Agent Reinforcement
Learning (MARL) is a research field that aims to find optimal solutions for
multiple agents that interact with each other. This work aims to give an
overview of the field to researchers in autonomous mobility. We first explain
MARL and introduce important concepts. Then, we discuss the central paradigms
that underlie MARL algorithms, and give an overview of state-of-the-art methods
and ideas in each paradigm. With this background, we survey applications of
MARL in autonomous mobility scenarios and give an overview of existing
scenarios and implementations.

该论文介绍了多智能体强化学习在自主移动中的应用，包括行为规划、车辆之间通信、性能提升等方面，并探讨了该领域的一些最新方法和想法。