mlsanityData to 7 Oct 2026

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library

arXiv 2210.13708 ↗First posted 11 Oct 2022cs.LG cs.AI cs.MAcited by 21

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Abstract (arXiv)

A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues. In this paper, we present MARLlib, a library designed to address the aforementioned challenge by leveraging three key mechanisms: 1) a standardized multi-agent environment wrapper, 2) an agent-level algorithm implementation, and 3) a flexible policy mapping strategy. By utilizing these mechanisms, MARLlib can effectively disentangle the intertwined nature of the multi-agent task and the learning process of the algorithm, with the ability to automatically alter the training strategy based on the current task's attributes. The MARLlib library's source code is publicly accessible on GitHub: urlhttps://github.com/Replicable-MARL/MARLlib.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

24 of 33 references.

References · 24

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  3. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  4. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  5. 2019The StarCraft Multi-Agent Challengecited by 589
  6. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  7. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  8. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 308
  9. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 257
  10. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  11. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  12. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 194
12 more
  1. 2021Trust Region Policy Optimisation in Multi-Agent Reinforcement Learningcited by 180
  2. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  3. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  4. 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 114
  5. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  6. 2020Game-Theoretic Multiagent Reinforcement Learningcited by 92
  7. 2017MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligencecited by 69
  8. 2021Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learningcited by 68
  9. 2020Value-Decomposition Multi-Agent Actor-Criticscited by 47
  10. 2018Pommerman: A Multi-Agent Playgroundcited by 30
  11. 2021MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learningcited by 23
  12. 2021The Neural MMO Platform for Massively Multiagent Researchcited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 9; 2026: 5.

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  1. 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAXcited by 59
  2. 2023BenchMARL: Benchmarking Multi-Agent Reinforcement Learningcited by 31
  3. 2025GEM: A Gym for Agentic LLMscited by 17
  4. 2024FightLadder: A Benchmark for Competitive Multi-Agent Reinforcement Learningcited by 13
  5. 2023Pearl: A Production-ready Reinforcement Learning Agentcited by 9
  6. 2024MAexp: A Generic Platform for RL-based Multi-Agent Explorationcited by 5
  7. 2023Major-Minor Mean Field Multi-Agent Reinforcement Learningcited by 4
  8. 2026A Reference Architecture of Reinforcement Learning Frameworkscited by 3
  9. 2025Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applicationscited by 1
  10. 2025Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Corecited by 1
  11. 2025GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspectivecited by 1
  12. 2025Learning to Lead Themselves: Agentic AI in MAS using MARLcited by 1
9 more
  1. 2024Multi-Agent Environments for Vehicle Routing Problemscited by 0
  2. 2025Dynamic Sight Range Selection in Multi-Agent Reinforcement Learningcited by 0
  3. 2025Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Controlcited by 0
  4. 2025An effective control of large systems of active particles: An application to evacuation problemcited by 0
  5. 2025StarCraft+: Benchmarking Multi-agent Algorithms in Adversary Paradigmcited by 0
  6. 2026MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Marketscited by 0
  7. 2026NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learningcited by 0
  8. 2026ACPO: Agent-Chained Policy Optimization for Multi-Agent Reinforcement Learningcited by 0
  9. 2026Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMscited by 0