DMS Statistics and Data Science Seminar

Speaker:  Andrea Angiuli (Research Scientist in the Prime Machine Learning team at Amazon)  

Title: Bridging the gap of reinforcement learning for mean field games and mean field control problems

Abstract: Mean field games (MFG) and mean field control problems (MFC) are frameworks to study Nash equilibria or social optima in games with a continuum of agents. These problems can be used to approximate competitive or cooperative games with a large finite number of agents and have found a broad range of applications, in particular in economics. In recent years, the question of learning in MFG and MFC has garnered interest, both as a way to compute solutions and as a way to model how large populations of learners converge to an equilibrium. Of particular interest is the setting where the agents do not know the model, which leads to the development of reinforcement learning (RL) methods. We present a two timescale approach with RL for MFG and MFC, which relies on a unified Q-learning algorithm. To illustrate this method, we apply it to mean field problems arising in Finance.