TY - GEN

T1 - Oblivious equilibrium for large-scale stochastic games with unbounded costs

AU - Adlakha, Sachin

AU - Johari, Ramesh

AU - Weintraub, Gabriel

AU - Goldsmith, Andrea

PY - 2008

Y1 - 2008

N2 - 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].

AB - 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].

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U2 - 10.1109/CDC.2008.4739389

DO - 10.1109/CDC.2008.4739389

M3 - Conference contribution

AN - SCOPUS:62949217777

SN - 9781424431243

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 5531

EP - 5538

BT - Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008

T2 - 47th IEEE Conference on Decision and Control, CDC 2008

Y2 - 9 December 2008 through 11 December 2008

ER -