Q-Learning in Regularized Mean-field Games

Berkay Anahtarci (), Can Deha Kariksiz () and Naci Saldi ()
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Berkay Anahtarci: Özyeğin University
Can Deha Kariksiz: Özyeğin University
Naci Saldi: Bilkent University

Dynamic Games and Applications, 2023, vol. 13, issue 1, No 5, 89-117

Abstract: Abstract 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.

Keywords: Mean-field games; Q-learning; Regularized Markov decision processes; Discounted reward (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13235-022-00450-2

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