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Q-Learning in Regularized Mean-field Games
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Q-Learning in Regularized Mean-field Games
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In this paper, we introduce a regularized mean-field game and study learning of this game under an infinite-horizon discounted reward function. Regularization is introduced by adding a strongly concave regularization function to the one-stage reward function in the classical mean-field game model. We establish a value iteration based learning algorithm to this regularized mean-field game using fitted Q-learning. The regularization term in general makes reinforcement learning algorithm more robust to the system components. Moreover, it enables us to establish error analysis of the learning algorithm without imposing restrictive convexity assumptions on the system components, which are needed in the absence of a regularization term.
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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 linea...
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