The idea of creating agents that automatically learn to play games through experience has been one of the major goals for game researchers. Simulation-based search and reinforcement learning have been two of the most active areas of research tackling this problem. One of the main challenges that links both areas is how to acquire domain knowledge that can. be effectively integrated into simulation-based search algorithms. In this thesis we address this challenge in several ways. First, we use temporal difference learning to find value functions in the form of weighted piece counters and N-tuple systems to play the game of Othello. Next, we present an algorithm that combines TD learning with coevolution to learn value functions of higher quality. These learned value functions Serve as basis to enhance the performance of Monte Carlo Tree Search by incorporating them in the tree and default policies. Finally, we conduct an extensive empirical analysis of Monte Carlo Tree Search by comparing it against other simulation-based and minimax search algorithms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:605563 |
| Date | January 2013 |
| Creators | Robles, David |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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