@Article{JML-2-2,
author = {Angiuli, Andrea and Nils, Detering and Jean-Pierre, Fouque and Mathieu, Laurière and Lin, Jimin},
title = {Reinforcement Learning Algorithm for Mixed Mean Field Control Games},
journal = {Journal of Machine Learning},
year = {2023},
volume = {2},
number = {2},
pages = {108--137},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2790-2048},
doi = {https://doi.org/10.4208/jml.220915},
url = {https://global-sci.com/article/87598/reinforcement-learning-algorithm-for-mixed-mean-field-control-games}
}