A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.
The Annals of Applied Probability 33(6B), 5334–5381 (2023)
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Mean Field Reinforcement Learning
A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.