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Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
[article]
2019
arXiv
pre-print
We study the performance of optimistic regret-minimization algorithms for both minimizing regret in, and computing Nash equilibria of, zero-sum extensive-form games. In order to apply these algorithms to extensive-form games, a distance-generating function is needed. We study the use of the dilated entropy and dilated Euclidean distance functions. For the dilated Euclidean distance function we prove the first explicit bounds on the strong-convexity parameter for general treeplexes. Furthermore,
arXiv:1910.10906v2
fatcat:umg5iz4iqnhlfddlrjwywxpave