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The Complexity of Infinite-Horizon General-Sum Stochastic Games

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abstract

We study the complexity of computing stationary Nash equilibrium (NE) in n-player infinite-horizon general-sum stochastic games. We focus on the problem of computing NE in such stochastic games when each player is restricted to choosing a stationary policy and rewards are discounted. First, we prove that computing such NE is in PPAD (in addition to clearly being PPAD-hard). Second, we consider turn-based specializations of such games where at each state there is at most a single player that can take actions and show that these (seemingly-simpler) games remain PPAD-hard. Third, we show that under further structural assumptions on the rewards computing NE in such turn-based games is possible in polynomial time. Towards achieving these results we establish structural facts about stochastic games of broader utility, including monotonicity of utilities under single-state single-action changes and reductions to settings where each player controls a single state.

fields

cs.GT 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Solving Zero-Sum Convex Markov Games

cs.GT · 2025-06-19 · conditional · novelty 7.0

Independent policy-gradient algorithms provably compute approximate Nash equilibria in two-player zero-sum convex Markov games.

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  • Solving Zero-Sum Convex Markov Games cs.GT · 2025-06-19 · conditional · none · ref 58 · internal anchor

    Independent policy-gradient algorithms provably compute approximate Nash equilibria in two-player zero-sum convex Markov games.