mlsanityData to 7 Oct 2026

Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects

arXiv 2203.10603 ↗First posted 20 Mar 2022cs.MA cs.AI cs.LGcited by 17

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Abstract (arXiv)

Significant advances have recently been achieved in Multi-Agent Reinforcement Learning (MARL) which tackles sequential decision-making problems involving multiple participants. However, MARL requires a tremendous number of samples for effective training. On the other hand, model-based methods have been shown to achieve provable advantages of sample efficiency. However, the attempts of model-based methods to MARL have just started very recently. This paper presents a review of the existing research on model-based MARL, including theoretical analyses, algorithms, and applications, and analyzes the advantages and potential of model-based MARL. Specifically, we provide a detailed taxonomy of the algorithms and point out the pros and cons for each algorithm according to the challenges inherent to multi-agent scenarios. We also outline promising directions for future development of this field.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 22 of its 44 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

22 of 44 references.

References · 22

  1. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  2. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  3. 2020QPLEX: Duplex Dueling Multi-Agent Q-Learningcited by 267
  4. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
  5. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 194
  6. 2019Benchmarking Model-Based Reinforcement Learningcited by 158
  7. 2019Mastering Complex Control in MOBA Games with Deep Reinforcement Learningcited by 150
  8. 2017Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problemscited by 126
  9. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  10. 2020Provable Self-Play Algorithms for Competitive Reinforcement Learningcited by 112
  11. 2020Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexitycited by 91
  12. 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 90
10 more
  1. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 81
  2. 2021Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learningcited by 68
  3. 2019AlphaStar: An Evolutionary Computation Perspectivecited by 50
  4. 2021Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learningcited by 25
  5. 2021Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rolloutscited by 24
  6. 2021Model-Based Opponent Modelingcited by 21
  7. 2019Multi-Agent Reinforcement Learning with Multi-Step Generative Modelscited by 18
  8. 2021MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelscited by 16
  9. 2021Model based Multi-agent Reinforcement Learning with Tensor Decompositionscited by 7
  10. 2021Robustness and sample complexity of model-based MARL for general-sum Markov gamescited by 6

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Citing papers per year: 2022: 3; 2023: 9; 2024: 1; 2025: 4.

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  1. 2022A Survey on Model-based Reinforcement Learningcited by 65
  2. 2023ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordinationcited by 23
  3. 2023Multi-agent Continual Coordination via Progressive Task Contextualizationcited by 10
  4. 2022Interaction Pattern Disentangling for Multi-Agent Reinforcement Learningcited by 8
  5. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  6. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5
  7. 2023Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learningcited by 4
  8. 2024Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimationcited by 4
  9. 2025SWIRL: A Staged Workflow for Interleaved Reinforcement Learning in Mobile GUI Controlcited by 4
  10. 2023CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learningcited by 3
  11. 2023VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Modelcited by 3
  12. 2025Social World Model-Augmented Mechanism Design Policy Learningcited by 3
5 more
  1. 2022Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learningcited by 2
  2. 2025Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Reviewcited by 1
  3. 2023Characterizing Speed Performance of Multi-Agent Reinforcement Learningcited by 0
  4. 2023Cooperation Dynamics in Multi-Agent Systems: Exploring Game-Theoretic Scenarios with Mean-Field Equilibriacited by 0
  5. 2025On Swarm Leader Identification using Probing Policiescited by 0