{"ID":5676026,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T22:43:54.027453447Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01525","arxiv_id":"2607.01525","title":"Mean Field Reinforcement Learning","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.","short_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 de...","url_abs":"https://arxiv.org/abs/2607.01525","url_pdf":"https://arxiv.org/pdf/2607.01525v1","authors":"[\"René Carmona\",\"Mathieu Laurière\"]","published":"2026-07-01T22:44:24Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.LG\",\"cs.MA\",\"math.PR\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}