Vairiational Stochastic Games

Zhiyu Zhao; Haifeng Zhang

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arXiv · 2503.06037

Vairiational Stochastic Games

Zhiyu Zhao·Haifeng Zhang

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Abstract

The Control as Inference (CAI) framework has successfully transformed single-agent reinforcement learning (RL) by reframing control tasks as probabilistic inference problems. However, the extension of CAI to multi-agent, general-sum stochastic games (SGs) remains underexplored, particularly in decentralized settings where agents operate independently without centralized coordination. In this paper, we propose a novel variational inference framework tailored to decentralized multi-agent systems. Our framework addresses the challenges posed by non-stationarity and unaligned agent objectives, proving that the resulting policies form an $\epsilon$-Nash equilibrium. Additionally, we demonstrate theoretical convergence guarantees for the proposed decentralized algorithms. Leveraging this framework, we instantiate multiple algorithms to solve for Nash equilibrium, mean-field Nash equilibrium, and correlated equilibrium, with rigorous theoretical convergence analysis.

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Zhiyu Zhao, Haifeng Zhang. 2025-03-08. Vairiational Stochastic Games. https://arxiv.org/abs/2503.06037

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