Mean Field Reinforcement Learning

• URL: http://arxiv.org/abs/2607.01525v1
• Date: Wed, 01 Jul 2026 22:44:24 GMT
• Title: Mean Field Reinforcement Learning
• Abstract summary: The goal is to give readers a coherent bridge between mean field control theory and reinforcement learning methodology.<n>The presentation includes dynamic programming principles, propagation-of-chaos limits, and theoretical analyses of Q-learning and policy-gradient methods.
• Score: 2.394379536305005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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