World-class interpretable poker
Name
10994_2022_Article_6179.pdf
Size
1.55 MB
Format
Unknown
Checksum (MD5)
49060c12724b6b2401350bd42f38cc1f
Author(s)
Bertsimas, Dimitris
Paskov, Alex
Date Issued
June 2022
Journal
Machine Learning
Publisher
Springer Science and Business Media LLC
Citation
Bertsimas, Dimitris and Paskov, Alex. 2022. "World-class interpretable poker."
Version
Final published version
Abstract
Abstract
We address the problem of interpretability in iterative game solving for imperfect-information games such as poker. This lack of interpretability has two main sources: first, the use of an uninterpretable feature representation, and second, the use of black box methods such as neural networks, for the fitting procedure. In this paper, we present advances on both fronts. Namely, first we propose a novel, compact, and easy-to-understand game-state feature representation for Heads-up No-limit (HUNL) Poker. Second, we make use of globally optimal decision trees, paired with a counterfactual regret minimization (CFR) self-play algorithm, to train our poker bot which produces an entirely interpretable agent. Through experiments against Slumbot, the winner of the most recent Annual Computer Poker Competition, we demonstrate that our approach yields a HUNL Poker agent that is capable of beating the Slumbot. Most exciting of all, the resulting poker bot is highly interpretable, allowing humans to learn from the novel strategies it discovers.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-022-06179-8
