Efficient Online Pruning and Abstraction for Imperfect Information Extensive-Form Games

Boning Li, Longbo Huang

Keywords: Game Theory, Imperfect Information Games, Counterfactual Regret Minimization, Poker, Machine Learning

Abstract:

Efficiently computing approximate equilibrium strategies in large Imperfect Information Extensive-Form Games (IIEFGs) poses significant challenges due to the game tree's exponential growth. While pruning and abstraction techniques are essential for complexity reduction, existing methods face two key limitations: (i) Seamless integration of pruning with Counterfactual Regret Minimization (CFR) is nontrivial, and (ii) Pruning and abstraction approaches incur prohibitive computational costs, hindering real-world deployment. We propose Expected-Value Pruning and Abstraction (EVPA), a novel online framework that addresses these challenges through three synergistic components: (i) Expected value estimation using approximate Nash equilibrium strategies to quantify information set utilities, (ii) Minimax pruning before CFR to eliminate a large number of sub-optimal actions permanently, and (iii) Dynamic online information abstraction merging information sets based on their current and future expected values in subgames. Experiments on Heads-up No-Limit Texas Hold'em (HUNL) show EVPA outperforms DeepStack's replication and Slumbot with significant win-rate margins in multiple settings. Remarkably, EVPA requires only $1$%-$2$% of the solving time to reach an approximate Nash equilibrium compared to DeepStack's replication.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 6996