Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions

Understand

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.