Beating the Pandemic: A Reinforcement Learning Approach to Cooperative Board Game Strategy
| carrollscholars.object.degree | Bachelor's | en_US |
| carrollscholars.object.department | Mathematics | en_US |
| carrollscholars.object.major | Mathematics | en_US |
| carrollscholars.object.season | Spring | en_US |
| dc.contributor.advisor | Wendt, Theodore | |
| dc.contributor.advisor | Fasteen, Jodi | |
| dc.contributor.advisor | Morris, Jeffrey | |
| dc.contributor.author | Jarman, Anna | |
| dc.date.accessioned | 2023-05-09T23:12:25Z | |
| dc.date.available | 2023-05-09T23:12:25Z | |
| dc.date.issued | 2023 | |
| dc.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. | en_US |
| dc.identifier.uri | https://scholars.carroll.edu/handle/20.500.12647/10620 | |
| dc.language.iso | en_US | en_US |
| dc.title | Beating the Pandemic: A Reinforcement Learning Approach to Cooperative Board Game Strategy | en_US |
| dc.type | Thesis | en_US |