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Financial Mathematics Seminar: Jean-Perre Fouque, University of California, Santa Barbara, Reinforcement Learning Algorithm for Mixed Mean Field Control Games
February 10 | 1:30 pm - 2:30 pm EST
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, Mathieu Laurière, and J. Lin.