Partially-Observed Discrete-Time Risk-Sensitive Mean-Field Games

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider in this paper a general class of discrete-time partially-observed mean-field games with Polish state, action, and measurement spaces and with risk-sensitive (exponential) cost functions which capture the risk-averse behaviour of each agent. As standard in mean-field game models, here each agent is weakly coupled with the rest of the population through its individual cost and state dynamics via the empirical distribution of the states. We first establish the mean-field equilibrium in the infinite-population limit by first transforming the risk-sensitive problem to one with risk-neutral (that is, additive instead of multiplicative) cost function, and then employing the technique of converting the underlying original partially-observed stochastic control problem to a fully observed one on the belief space and the principle of dynamic programming. Then, we show that the mean-field equilibrium policy, when adopted by each agent, forms an approximate Nash equilibrium for games with sufficiently many agents.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113982
StatePublished - Dec 2019
Externally publishedYes
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference58th IEEE Conference on Decision and Control, CDC 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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