The Complexity of Infinite-Horizon General-Sum Stochastic Games
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2022.
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Citing papers per year: 2022: 4; 2023: 5; 2024: 1.
10 papers.
- 2022Policy Optimization for Markov Games: Unified Framework and Faster Convergencecited by 16
- 2022Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Modelcited by 15
- 2022On the convergence of policy gradient methods to Nash equilibria in general stochastic gamescited by 13
- 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Gamescited by 9
- 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactionscited by 8
- 2023A Reduction-based Framework for Sequential Decision Making with Delayed Feedbackcited by 7
- 2022Efficiently Computing Nash Equilibria in Adversarial Team Markov Gamescited by 6
- 2024The complexity of approximate (coarse) correlated equilibrium for incomplete information gamescited by 4
- 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observationcited by 2
- 2023Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Propertycited by 2
- 2024The complexity of approximate (coarse) correlated equilibrium for incomplete information games4 Jun 2024
- 2023Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property19 Dec 2023
- 2023Posterior Sampling for Competitive RL: Function Approximation and Partial Observation30 Oct 2023
- 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactions13 Jul 2023
- 2023Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games22 Mar 2023
- 2023A Reduction-based Framework for Sequential Decision Making with Delayed Feedback3 Feb 2023
- 2022On the convergence of policy gradient methods to Nash equilibria in general stochastic games17 Oct 2022
- 2022Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model22 Aug 2022
- 2022Efficiently Computing Nash Equilibria in Adversarial Team Markov Games3 Aug 2022
- 2022Policy Optimization for Markov Games: Unified Framework and Faster Convergence6 Jun 2022