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ATAL
2008
Springer

Stochastic search methods for nash equilibrium approximation in simulation-based games

8 years 4 months ago
Stochastic search methods for nash equilibrium approximation in simulation-based games
We define the class of games called simulation-based games, in which the payoffs are available as an output of an oracle (simulator), rather than specified analytically or using a payoff matrix. We then describe a convergent algorithm based on a hierarchical application of simulated annealing for estimating Nash equilibria (if they exist) in simulation-based games with finite-dimensional strategy sets. Additionally, we present alternative algorithms for best response and Nash equilibrium estimation, with a particular focus on one-shot infinite games of incomplete information. Our experimental results demonstrate that all the approaches we introduce are efficacious, albeit some more so than others. We show, for example, that while iterative best response dynamics has relatively weak convergence guarantees, it outperforms our convergent method experimentally. Additionally, we provide considerable evidence that a method based on random search outperforms gradient descent in our setting.
Yevgeniy Vorobeychik, Michael P. Wellman
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Yevgeniy Vorobeychik, Michael P. Wellman
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