Learning in discrete-time average-cost mean-field games
Type :
Conference paper
Publication Status :
Published
Access :
restrictedAccess
Abstract
In this paper, we consider learning of discrete-time mean-field games under an average cost criterion. We propose a Q-iteration algorithm via Banach Fixed Point Theorem to compute the mean-field equilibrium when the model is known. We then extend this algorithm to the learning setting by using fitted Q-iteration and establish the probabilistic convergence of the proposed learning algorithm. Our work on learning in average-cost mean-field games appears to be the first in the literature.
Source :
2021 60th IEEE Conference on Decision and Control (CDC)
Date :
2021
Volume :
2021-December
Publisher :
IEEE
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