Independent Reinforcement Learning in Discounted Markov Games

Asrin Efe Yorulmaz; Ugur Aydin; Tamer Basar

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arXiv · 2609.00504

Independent Reinforcement Learning in Discounted Markov Games

Asrin Efe Yorulmaz·Ugur Aydin·Tamer Basar

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Abstract

In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``$\mathsf{ETH}$ for $\mathsf{PPAD}$", we show that, for every fixed discount factor, there is no polynomial-time algorithm for computing inverse-polynomially accurate coarse correlated equilibria in discounted general-sum Markov games when players learn independently in decentralized settings. Complementing this hardness result, we provide what appears to be the first \emph{radically uncoupled} algorithm with sub-exponential convergence guarantees to coarse correlated equilibria in discounted general-sum Markov games without imposing any structural restrictions on the game. Our algorithm is a \emph{layered} variant of optimistic mirror descent with an increasing step-size schedule tailored to the multi-agent setting. Finally, we develop both full-feedback and partial feedback versions of the aforementioned algorithm and establish sub-exponential convergence guarantees for each case.

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BibTeX RIS

Asrin Efe Yorulmaz, Ugur Aydin, Tamer Basar. 2026-09-01. Independent Reinforcement Learning in Discounted Markov Games. https://arxiv.org/abs/2609.00504

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