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Q-Learning in Regularized Mean-field Games. (arXiv:2003.12151v3 [math.OC] UPDATED)
Nov. 11, 2022, 2:12 a.m. | Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
cs.LG updates on arXiv.org arxiv.org
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 …
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