Oblivious equilibrium for large-scale stochastic games with unbounded costs

Sachin Adlakha, Ramesh Johari, Gabriel Weintraub, Andrea Goldsmith

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

35 Scopus citations

Abstract

We study stochastic dynamic games with a large number of players, where players are coupled via their cost functions. A standard solution concept for stochastic games is Markov perfect equilibrium (MPE). In MPE, each player's strategy is a function of its own state as well as the state of the other players. This makes MPE computationally prohibitive as the number of players becomes large. An approximate solution concept called oblivious equilibrium (OE) was introduced in [1], where each player's decision depends only on its own state and the "long-run average" state of other players. This makes OE computationally more tractable than MPE. It was shown in [1] that, under a set of assumptions, as the number of players become large, OE closely approximates MPE. In this paper we relax those assumptions and generalize that result to cases where the cost functions are unbounded. Furthermore, we show that under these relaxed set of assumptions, the OE approximation result can be applied to large population linear quadratic Gaussian (LQG) games [2].

Original languageEnglish (US)
Title of host publicationProceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
Pages5531-5538
Number of pages8
DOIs
StatePublished - 2008
Externally publishedYes
Event47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico
Duration: Dec 9 2008Dec 11 2008

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other47th IEEE Conference on Decision and Control, CDC 2008
Country/TerritoryMexico
CityCancun
Period12/9/0812/11/08

All Science Journal Classification (ASJC) codes

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

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