Year: 2023
Author: Andrea Angiuli, Nils Detering, Jean-Pierre Fouque, Mathieu Laurière, Jimin Lin
Journal of Machine Learning, Vol. 2 (2023), Iss. 2 : pp. 108–137
Abstract
We present a new combined mean field control game (MFCG) problem which can be interpreted as a competitive game between collaborating groups and its solution as a Nash equilibrium between groups. Players coordinate their strategies within each group. An example is a modification of the classical trader’s problem. Groups of traders maximize their wealth. They face cost for their transactions, for their own terminal positions, and for the average holding within their group. The asset price is impacted by the trades of all agents. We propose a three-timescale reinforcement learning algorithm to approximate the solution of such MFCG problems. We test the algorithm on benchmark linear-quadratic specifications for which we provide analytic solutions.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jml.220915
Journal of Machine Learning, Vol. 2 (2023), Iss. 2 : pp. 108–137
Published online: 2023-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 30
Keywords: Mean Field Control Games Reinforcement Learning Q-Learning Optimal Liquidation.
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