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| Title: |
Learning to play Monopoly: A Reinforcement Learning approach |
| Author(s): |
P. Bailis, A. Fachantidis, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file.
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| Keywords: |
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| Appeared in: |
AISB 2014: AI & GAMES, (in press), London, U.K., 2014.
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| Abstract: |
Reinforcement Learning is a rather popular machine learning paradigm which relies on an agent interacting with an environment and learning through trial and error to maximize the cummulative sum of rewards received by it. In this paper, we are proposing a novel representation of the famous board game Monopoly as a Markov Decision Process and a Reinforcement Learning agent capable of playing and learning winning strategies. The conclusions drawn from the experiments are particularly positive, since the proposed agent demonstrated intelligent behavior and high win rates against different types of agent-players. |
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