Paper Info
Title
Approximate Markov-Nash Equilibria For Discrete-Time Risk-Sensitive Mean-Field Games
Abstract
In this paper, we study a class of discrete-time mean-field games under the infinite-horizon risk-sensitive optimality criterion. Risk sensitivity is introduced for each agent (player) via an exponential utility function. In this game model, each agent is coupled with the rest of the population through the empirical distribution of the states, which affects both the agent's individual cost and its state dynamics. Under mild assumptions, we establish the existence of a mean-field equilibrium in the infinite-population limit as the number of agents (N) goes to infinity, and we then show that the policy obtained from the mean-field equilibrium constitutes an approximate Nash equilibrium when N is sufficiently large.
Year
DOI
Venue
2020
10.1287/moor.2019.1044
MATHEMATICS OF OPERATIONS RESEARCH
Keywords
DocType
Volume
mean-field games, approximate Nash equilibrium, risk-sensitive stochastic control
Journal
45
Issue
ISSN
Citations 
4
0364-765X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Naci Saldi12910.27
Tamer Basar23497402.11
Maxim Raginsky377160.65