mlsanityData to 14 Oct 2026

Mean Field Reinforcement Learning

arXiv 2607.01525 ↗First posted 1 Jul 2026math.OC cs.LG cs.MAcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mean field interactions and common noise. Starting from the connection between multi-agent reinforcement learning and mean field control, it develops the probabilistic, mathematical, and control-theoretic framework needed to formulate representative-agent learning problems, analyze their relationship with finite-population systems, and study both general and linear-quadratic models. The presentation includes dynamic programming principles, propagation-of-chaos limits, and theoretical analyses of tabular Q-learning and policy-gradient methods. It also discusses numerical implementations, including tabular schemes and deep reinforcement learning methods such as deep deterministic policy gradient. The goal is to give readers a coherent bridge between mean field control theory and reinforcement learning methodology, emphasizing the mathematical structure of the problems and the design of tractable learning approaches for large stochastic populations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 165 references that are in the map. First posted on arXiv 1 Jul 2026.

Builds on

12 of 165 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Mean-Field PhiBE: Continuous-Time Mean-Field Reinforcement Learning from Discrete-Time Data25 Jun
  2. 2026Model-free policy gradient for discrete-time mean-field control16 Jan
  3. 2025Robust Mean Field Social Control: A Unified Reinforcement Learning Framework27 Feb
  4. 2024Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise5 Aug
  5. 2024Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective17 Mar

Other references · 7

  1. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  2. 2022Learning in Mean Field Games: A Surveycited by 61
  3. 2018Lyapunov Theory for Discrete Time Systemscited by 16
  4. 2021Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approachcited by 14
  5. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 14
  6. 2023Convergence of Multi-Scale Reinforcement Q-Learning Algorithms for Mean Field Game and Control Problemscited by 10
  7. 2025Reconciling Discrete-Time Mixed Policies and Continuous-Time Relaxed Controls in Reinforcement Learning and Stochastic Controlcited by 3

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  1. 2024Efficient and Scalable Deep Reinforcement Learning for Mean Field Control Games
  2. 2024Mean Field LQG Social Optimization: A Reinforcement Learning Approach
  3. 2025Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
  4. 2024Unified continuous-time q-learning for mean-field game and mean-field control problems
  5. 2024When is Mean-Field Reinforcement Learning Tractable and Relevant?
  6. 2026Towards Scaling Reinforcement Learning to Massive Populations: Learning Mean-Field Representations
  7. 2026Actor-Critic Learning for Extended Mean Field Control with Deterministic Policies
  8. 2024Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning