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.
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
We propose a decentralized multi-agent reinforcement learning algorithm for stochastic games with continuous state spaces.
Abstract
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Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
We propose a decentralized multi-agent reinforcement learning algorithm for stochastic games with continuous state spaces.
Cite
@inproceedings{altabaa2023decentralized,
author = {Altabaa, Awni and Yongacoglu, Bora and Yüksel, Serdar},
booktitle = {2023 American Control Conference (ACC)},
title = {Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games},
year = {2023},
}