mlsanityData to 7 Oct 2026

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

arXiv 2203.07676 ↗First posted 15 Mar 2022cs.AI cs.MAcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 38 of its 74 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

38 of 74 references.

References · 38

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  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
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  9. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
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  11. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 281
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26 more
  1. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  2. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  3. 2019MAVEN: Multi-Agent Variational Explorationcited by 181
  4. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 173
  5. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  6. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 159
  7. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  8. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  9. 2018Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Taskscited by 114
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  11. 2019Shapley Q-value: A Local Reward Approach to Solve Global Reward Gamescited by 71
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  14. 2019Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learningcited by 57
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  20. 2020Flatland-RL : Multi-Agent Reinforcement Learning on Trainscited by 32
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  22. 2019A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Networkcited by 22
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Citing papers per year: 2022: 4; 2023: 3; 2024: 7; 2025: 2.

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  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  2. 2022Graph Neural Networks in IoT: A Surveycited by 27
  3. 2023Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learningcited by 13
  4. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
  5. 2024A Review of Nine Physics Engines for Reinforcement Learning Researchcited by 9
  6. 2023Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attentioncited by 7
  7. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
  8. 2024The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learningcited by 5
  9. 2022How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategiescited by 4
  10. 2023Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learningcited by 3
  11. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspectivecited by 2
  12. 2022Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learningcited by 1
4 more
  1. 2024Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learningcited by 1
  2. 2024A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learningcited by 0
  3. 2024Spatio-temporal Value Semantics-based Abstraction for Dense Deep Reinforcement Learningcited by 0
  4. 2024Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approachcited by 0