Nonlinear programming and stationary equilibria in stochastic games

Stationary equilibria in discounted and limiting average finite state/action space stochastic games are shown to be equivalent to global optima of certain nonlinear programs. For zero sum limiting average games, this formulation reduces to a linear objective, nonlinear constraints program, which finds the “best” stationary strategies, even whenε-optimal stationary strategies do not exist, for arbitrarily smallε.

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