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      Q-learning in regularized mean-field games

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      Author(s)
      Anahtarci, B.
      Kariksiz, C.D.
      Saldi, Naci
      Date
      2022-05-23
      Source Title
      Dynamic Games and Applications
      Print ISSN
      2153-0785
      Electronic ISSN
      2153-0793
      Publisher
      Birkhaeuser Science
      Pages
      1 - 29
      Language
      English
      Type
      Article
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      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
      Permalink
      http://hdl.handle.net/11693/111428
      Published Version (Please cite this version)
      https://www.doi.org/10.1007/s13235-022-00450-2
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