Limit theory for controlled McKean-Vlasov dynamics
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Abstract (arXiv)
This paper rigorously connects the problem of optimal control of McKean-Vlasov dynamics with large systems of interacting controlled state processes. Precisely, the empirical distributions of near-optimal control-state pairs for the n-state systems, as n tends to infinity, admit limit points in distribution (if the objective functions are suitably coercive), and every such limit is supported on the set of optimal control-state pairs for the McKean-Vlasov problem. Conversely, any distribution on the set of optimal control-state pairs for the McKean-Vlasov problem can be realized as a limit in this manner. Arguments are based on controlled martingale problems, which lend themselves naturally to existence proofs; along the way it is shown that a large class of McKean-Vlasov control problems admit optimal Markovian controls.
Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). First posted on arXiv 26 Sep 2016.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 4; 2025: 0; 2026: 2.
11 papers.
- 2021Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 38
- 2021On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)cited by 31
- 2023Continuous-time q-learning for mean-field control problemscited by 17
- 2022Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Banditscited by 10
- 2024Unified continuous-time q-learning for mean-field game and mean-field control problemscited by 7
- 2024Score-based Neural Ordinary Differential Equations for Computing Mean Field Control Problemscited by 7
- 2023Safe Model-Based Multi-Agent Mean-Field Reinforcement Learningcited by 3
- 2024Convergence of the Deep Galerkin Method for Finite State Mean Field Control Problemscited by 2
- 2026Stochastic Control Methods for Optimizationcited by 1
- 2026Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Datacited by 1
- 2024Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample sizecited by 0
- 2026Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data25 Jun 2026
- 2026Stochastic Control Methods for Optimization3 Jan 2026
- 2024Score-based Neural Ordinary Differential Equations for Computing Mean Field Control Problems24 Sep 2024
- 2024Unified continuous-time q-learning for mean-field game and mean-field control problems5 Jul 2024
- 2024Convergence of the Deep Galerkin Method for Finite State Mean Field Control Problems22 May 2024
- 2024Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample size8 Apr 2024
- 2023Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning29 Jun 2023
- 2023Continuous-time q-learning for mean-field control problems28 Jun 2023
- 2022Indexability is Not Enough for Whittle: Improved, Near-Optimal Algorithms for Restless Bandits31 Oct 2022
- 2021On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)9 Sep 2021
- 2021Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning8 Jul 2021