Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker

Tarik Začiragić, Aske Plaat, Joost Batenburg

Track: Type A (Regular Papers)

Keywords: Poker, Bluffing, Reinforcement Learning, Game Theory

Abstract:

In the game of poker, being unpredictable, or bluffing, is
an essential skill. When humans play poker, they bluff. However, most
works on computer-poker focus on performance metrics such as win rates,
while bluffing is overlooked. In this paper we study whether two popular
algorithms, DQN (based on reinforcement learning) and CFR (based on
game theory), exhibit bluffing behavior in Leduc Hold’em, a simplified
version of poker. We designed an experiment where we let the DQN
and CFR agent play against each other while we log their actions. We
find that both DQN and CFR exhibit bluffing behavior, but they do so
in different ways. Although both attempt to perform bluffs at different
rates, the percentage of successful bluffs (where the opponent folds) is
roughly the same. This suggests that bluffing is an essential aspect of the
game, not of the algorithm. Future work should look at different bluffing
styles and at the full game of poker.

Serve As Reviewer: ~Tarik_Začiragić1

Submission Number: 8