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Many scenarios in mobility and traffic involve multiple different agents thatneed to cooperate to find a joint solution. Recent advances in behavioralplanning use Reinforcement Learning to find effective and performant behaviorstrategies. However, as autonomous vehicles and vehicle-to-X communicationsbecome more mature, solutions that only utilize single, independent agentsleave potential performance gains on the road. Multi-Agent ReinforcementLearning (MARL) is a research field that aims to find optimal solutions formultiple agents that interact with each other. This work aims to give anoverview of the field to researchers in autonomous mobility. We first explainMARL and introduce important concepts. Then, we discuss the central paradigmsthat underlie MARL algorithms, and give an overview of state-of-the-art methodsand ideas in each paradigm. With this background, we survey applications ofMARL in autonomous mobility scenarios and give an overview of existingscenarios and implementations.

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility