Applied Mathematics Colloquia with Jean-Pierre Fouque: "Reinforcement Learning Algorithm for Mixed Mean Field Control Games"

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RE 104

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Speaker: Jean-Pierre Fouque, Distinguished Professor, Statistics & Applied Probability/Co-Director of the Center for Financial Mathematics and Actuarial Research, University of California Santa Barbara

Title: Reinforcement Learning Algorithm for Mixed Mean Field Control Games

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, Mathieu Laurière, and J. Lin.

 

Applied Mathematics Colloquia

 

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