California State University San Bernardino
Department of Computer Science and Engineering
Masters Project Presentation

Date November 14, 2008
Time 1:00pm-2:00pm

Place JBH 359

Title Implementation of Reinforcement Learning in Game Strategy Design

Candidate Chien-Yu Lin

Advisor Dr. Haiyan Qiao

Committee Members Dr. Ernesto Gomez Dr. David A. Turner

Abstract

Reinforcement learning is one type of machine learning. It is 
concerned with learning through the use of penalties and rewards. The main
purpose of this project is to apply reinforcement learning in the design of
game strategy. Using this approach, the computer learns the opponent 
strategy, and learning takes place during each step of play. The 
reinforcement learning used in this project will be based on the Q-learning
algorithm, and the game Blackjack is selected as the study model because of
its simplicity and popularity.
In order to demonstrate that the strategy implemented with reinforcement 
learning performs better than pre-programmed strategies, an experimental 
approach is used. In the experiments, the winning percentages of different
strategies with and without learning capabilities are compared when playing
Blackjack.