A Reinforcement Learning Agent for 1-Card Poker

Abstract

Modeling and reasoning about an opponent in a competitive environment is a difficult task. This paper uses a reinforcement learning framework to build an adaptable agent for the game of 1-card poker. The resulting agent is evaluated against various opponents and is shown to be very competitive.

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Cite this paper

@inproceedings{Wahab2005ARL, title={A Reinforcement Learning Agent for 1-Card Poker}, author={Matthew Wahab}, year={2005} }