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MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search

arXiv 2404.03101 ↗First posted 3 Apr 2024cs.MA cs.LGcited by 0

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Abstract (arXiv)

Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge efficiently. In this paper, we propose a general training framework, MARL-LNS, to algorithmically address these issues by training on alternating subsets of agents using existing deep MARL algorithms as low-level trainers, while not involving any additional parameters to be trained. Based on this framework, we provide three algorithm variants based on the framework: random large neighborhood search (RLNS), batch large neighborhood search (BLNS), and adaptive large neighborhood search (ALNS), which alternate the subsets of agents differently. We test our algorithms on both the StarCraft Multi-Agent Challenge and Google Research Football, showing that our algorithms can automatically reduce at least 10% of training time while reaching the same final skill level as the original algorithm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 3 Apr 2024.

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22 of 38 references.

References · 22

  1. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  6. 2019The StarCraft Multi-Agent Challengecited by 597
  7. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  8. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  9. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  10. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 87
  11. 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAXcited by 63
  12. 2017Guided Deep Reinforcement Learning for Swarm Systemscited by 44
10 more
  1. 2021Learning a Large Neighborhood Search Algorithm for Mixed Integer Programscited by 43
  2. 2020Human-Level Performance in No-Press Diplomacy via Equilibrium Searchcited by 41
  3. 2020Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learningcited by 38
  4. 2013On the Complexity Analysis of Randomized Block-Coordinate Descent Methodscited by 21
  5. 2023Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Explorationcited by 18
  6. 2021Coordinated Proximal Policy Optimizationcited by 16
  7. 2021Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid Worldcited by 10
  8. 2022E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidancecited by 6
  9. 2023Towards Efficient Multi-Agent Learning Systemscited by 4
  10. 2021Temporal Induced Self-Play for Stochastic Bayesian Gamescited by 2

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  4. 2024YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
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