Fictitious Play in Markov Games with Single Controller
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Abstract (arXiv)
Certain but important classes of strategic-form games, including zero-sum and identical-interest games, have the fictitious-play-property (FPP), i.e., beliefs formed in fictitious play dynamics always converge to a Nash equilibrium (NE) in the repeated play of these games. Such convergence results are seen as a (behavioral) justification for the game-theoretical equilibrium analysis. Markov games (MGs), also known as stochastic games, generalize the repeated play of strategic-form games to dynamic multi-state settings with Markovian state transitions. In particular, MGs are standard models for multi-agent reinforcement learning -- a reviving research area in learning and games, and their game-theoretical equilibrium analyses have also been conducted extensively. However, whether certain classes of MGs have the FPP or not (i.e., whether there is a behavioral justification for equilibrium analysis or not) remains largely elusive. In this paper, we study a new variant of fictitious play dynamics for MGs and show its convergence to an NE in n-player identical-interest MGs in which a single player controls the state transitions. Such games are of interest in communications, control, and economics applications. Our result together with the recent results in [Sayin et al. 2020] establishes the FPP of two-player zero-sum MGs and n-player identical-interest MGs with a single controller (standing at two different ends of the MG spectrum from fully competitive to fully cooperative).
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2022.
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Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 2.
9 papers.
- 2022Independent and Decentralized Learning in Markov Potential Gamescited by 19
- 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Gamescited by 15
- 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactionscited by 8
- 2023Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Propertycited by 2
- 2023Logit-Q Dynamics for Efficient Learning in Stochastic Teamscited by 1
- 2024Paths to Equilibrium in Gamescited by 1
- 2025Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspectivecited by 0
- 2026Actor-Dual-Critic Dynamics for Zero-sum and Identical-Interest Stochastic Gamescited by 0
- 2026Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attackscited by 0
- 2026Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks6 Aug 2026
- 2026Actor-Dual-Critic Dynamics for Zero-sum and Identical-Interest Stochastic Games31 Jan 2026
- 2025Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspective27 Sep 2025
- 2024Paths to Equilibrium in Games26 Mar 2024
- 2023Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property19 Dec 2023
- 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactions13 Jul 2023
- 2023A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games3 Mar 2023
- 2023Logit-Q Dynamics for Efficient Learning in Stochastic Teams20 Feb 2023
- 2022Independent and Decentralized Learning in Markov Potential Games29 May 2022