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papersTODAY 04:00 UTC

Hierarchical Deep Counterfactual Regret Minimization for Imperfect Information Games

A revised arXiv paper presents a hierarchical deep learning approach to counterfactual regret minimization, the algorithm family widely used to solve imperfect information games. The authors combine deep networks with a hierarchical structure intended to handle large game trees and skill-based strategy learning. It is a research contribution rather than a released product or model.

arxiv/game-theory/reinforcement-learning/cfr