Regret Minimization in Behaviorally-Constrained Zero-Sum Games

ICML 2017 Gabriele FarinaChristian KroerTuomas Sandholm

No-regret learning has emerged as a powerful tool for solving extensive-form games. This was facilitated by the counterfactual-regret minimization (CFR) framework, which relies on the instantiation of regret minimizers for simplexes at each information set of the game... (read more)

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