Correlated equilibria and mean field games: a simple model
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Abstract (arXiv)
In the context of simple finite-state discrete time systems, we introduce a generalization of mean field game solution, called correlated solution, which can be seen as the mean field game analogue of a correlated equilibrium. Our notion of solution is justified in two ways: We prove that correlated solutions arise as limits of exchangeable correlated equilibria in restricted (Markov open-loop) strategies for the underlying N-player games, and we show how to construct approximate N-player correlated equilibria starting from a correlated solution to the mean field game.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2020.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 2.
6 papers.
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
- 2022Learning Correlated Equilibria in Mean-Field Gamescited by 4
- 2023Major-Minor Mean Field Multi-Agent Reinforcement Learningcited by 4
- 2022Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Controlcited by 1
- 2024Mean Field Correlated Imitation Learningcited by 0
- 2024Bounded Rationality Equilibrium Learning in Mean Field Gamescited by 0
- 2024Bounded Rationality Equilibrium Learning in Mean Field Games11 Nov 2024
- 2024Mean Field Correlated Imitation Learning14 Apr 2024
- 2023Major-Minor Mean Field Multi-Agent Reinforcement Learning19 Mar 2023
- 2022Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control15 Sep 2022
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning8 Sep 2022
- 2022Learning Correlated Equilibria in Mean-Field Games22 Aug 2022