Independent Reinforcement Learning in Discounted Markov Games

• URL: http://arxiv.org/abs/2609.00504v1
• Date: Tue, 01 Sep 2026 00:07:45 GMT
• Title: Independent Reinforcement Learning in Discounted Markov Games
• Abstract summary: We show for every fixed discount factor, there is no-time algorithm for inverse-polynomially accurate coarse correlated equilibria in discounted general-sum Markov games.<n>Our algorithm is a emphlayered variant of optimistic mirror descent with an increasing step-size schedule tailored to the multi-agent setting.
• Score: 5.5438676149999075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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