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Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games

arXiv 2303.13539 ↗First posted 16 Mar 2023cs.LG cs.GTcited by 0

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Abstract (arXiv)

Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL). Recent advances in MARL have focused primarily on games with finitely many states. In this work, we study multi-agent learning in stochastic games with general state spaces and an information structure in which agents do not observe each other's actions. In this context, we propose a decentralized MARL algorithm and we prove the near-optimality of its policy updates. Furthermore, we study the global policy-updating dynamics for a general class of best-reply based algorithms and derive a closed-form characterization of convergence probabilities over the joint policy space.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 16 Mar 2023.

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References · 12

  1. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  2. 2021Independent Policy Gradient Methods for Competitive Reinforcement Learningcited by 97
  3. 2021Decentralized Q-Learning in Zero-sum Markov Gamescited by 53
  4. 2015Decentralized Q-Learning for Stochastic Teams and Gamescited by 34
  5. 2021Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stabilitycited by 20
  6. 2021Independent Learning in Stochastic Gamescited by 20
  7. 2019Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Informationcited by 12
  8. 2015Asymptotic Optimality of Finite Approximations to Markov Decision Processes with Borel Spacescited by 11
  9. 2021Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Gamescited by 9
  10. 2021Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuitycited by 7
  11. 2015Finite Model Approximations and Asymptotic Optimality of Quantized Policies in Decentralized Stochastic Controlcited by 1
  12. 2018A Universal Dynamic Program and Refined Existence Results for Decentralized Stochastic Controlcited by 1

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