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game-trees

topic2 events
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
papersSEP 12 04:00 UTC

GPU-CFR Compiles Game Trees to Dataflow for 80x Faster Regret Minimization

A new arXiv paper presents GPU-CFR, a technique that compiles game trees into static dataflow graphs so that counterfactual regret minimization can run efficiently on GPUs. The approach uses CUDA graph replay to reduce the overhead of the many small, interdependent memory operations that previously made CPUs faster for this workload. The authors report speedups of up to 80x over existing implementations.

research/gpu/game-theory/optimization