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.
@article{arxiv.2303.13539,
title = {Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games},
author = {Awni Altabaa and Bora Yongacoglu and Serdar Yüksel},
journal= {arXiv preprint arXiv:2303.13539},
year = {2024}
}