Beating the Pandemic: A Reinforcement Learning Approach to Cooperative Board Game Strategy
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Authors
Jarman, Anna
Advisor
Wendt, Theodore
Fasteen, Jodi
Morris, Jeffrey
Fasteen, Jodi
Morris, Jeffrey
Editor
Date of Issue
2023
Subject Keywords
Publisher
Citation
Series/Report No.
item.page.identifier
Title
Beating the Pandemic: A Reinforcement Learning Approach to Cooperative Board Game Strategy
Other Titles
Type
Thesis
Description
Abstract
Pandemic: Hot Zone - North America is an intricate board game in which players work together to treat disease and research cures for those diseases [3]. The game is part strategy and part luck. In this project we attempt to create an algorithm that will provide the best strategy for playing Pandemic. First, an optimization approach is taken in which we define na¨ıve strategies and analyze their effectiveness. Then, a reinforcement learning approach is taken in which we train an agent to play Pandemic in the same way humans learn to interact with our environment. The implementation of reinforcement learning in the context of this project is described in depth, including attempts to deal with a large state space and the use of hierarchical reinforcement learning. The results from this implementation are discussed and plans for future research and improvement are devised.
Sponsors
Degree Awarded
Bachelor's
Semester
Spring
Department
Mathematics