You are cordially invited to the Analysis Seminar organized by the Department of Mathematics.
Speaker: Naci Saldı (Bilkent)
“Learning in Discounted-cost and Average-cost Mean-field Games”
Abstract: In this talk, we consider learning approximate Nash equilibria for discrete-time mean-field games with nonlinear stochastic state dynamics subject to both average and discounted costs. To this end, we introduce a mean-field equilibrium (MFE) operator, whose fixed point is a mean-field equilibrium (i.e. equilibrium in the infinite population limit). We first prove that this operator is a contraction, and propose a learning algorithm to compute an approximate mean-field equilibrium by approximating the MFE operator with a random one. Moreover, using the contraction property of the MFE operator, we establish the error analysis of the proposed learning algorithm. We then show that the learned mean-field equilibrium constitutes an approximate Nash equilibrium for finite-agent games.
Date: Thursday, March 3, 2022
Time: 18:00-19:00, GMT+3.
This is an online seminar. To request the event link, please send a message to email@example.com