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Machine Learning and Mean-Field Games
Reinforcement Learning Algorithm for Mixed Mean Field Control Games
Jean-Pierre Fouque, University of California, Santa Barbara (UCSB)
Thursday, May 26, 2022
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 the groups. Within each group the players coordinate their strategies. An example of such a situation is a modification of the classical trader's problem. Groups of traders maximize their wealth. They are faced with transaction cost for their own trades and a cost for their own terminal position. In addition they face a cost for the average holding within their group. The asset price is impacted by the trades of all agents. We propose a reinforcement learning algorithm to approximate the solution of such mixed Mean Field Control Game problems. We test the algorithm on benchmark linear-quadratic specifications for which we have analytic solutions.
Joint work with A. Angiuli, N. Detering, and J. Lin