The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.
arXiv preprint arXiv:2312.06659 (2023)
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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.
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Continuous-time q-learning for mean-field control with common noise, part-II: q-learning algorithms
The authors propose actor-critic q-learning algorithms for mean-field control with common noise based on martingale orthogonality conditions and relaxed controls, establish convergence of inner iterations in the linear-quadratic case, and demonstrate performance on examples.
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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.