Mean Field Reinforcement Learning
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 ...
| Authors | René Carmona, Mathieu Laurière |
|---|---|
| Primary category | math.OC |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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