On Creating Human Models in Poker with Deep Learning and Regularized Search
Name
chen-nathanlc-meng-eecs-2026-thesis.pdf
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9.09 MB
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380ac5247280016d78cfd99648b18605
Author(s)
Chen, Nathan
Advisor(s)
Farina, Gabriele
Date Issued
February 2026
Publisher
Massachusetts Institute of Technology
Abstract
We explore the creation of human models of poker players with a combination of deep neural networks and regularized search. We begin with behavioral cloning and explain our neural architecture choices. Next, we qualitatively show that our model can express a variety of human-like behaviors. Finally, we use the aforementioned model combined with regularized search to understand the tradeoff between human-likeness and policy strength in poker. In particular, we find that we can increase the human-likeness of a near-equilibrium policy for a low cost in exploitability, or increase the strength of a human-like policy for a low cost in human prediction accuracy. This thesis lays groundwork for using computers to create human-like opponents to train against.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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