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Neural-fitted TD-leaf learning for playing Othello with structured neural networks.

Authors
  • van den Dries, Sjoerd
  • Wiering, Marco A
Type
Published Article
Journal
IEEE Transactions on Neural Networks and Learning Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Nov 01, 2012
Volume
23
Issue
11
Pages
1701–1713
Identifiers
DOI: 10.1109/TNNLS.2012.2210559
PMID: 24808066
Source
Medline
License
Unknown

Abstract

This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network connectivity patterns are used to decrease the number of learning parameters and to deal more effectively with the structural credit assignment problem, which is to change individual network weights based on the obtained feedback. Furthermore, the structured neural networks are trained with the novel neural-fitted temporal difference (TD) learning algorithm to create a system that can exploit most of the training experiences and enhance learning speed and performance. Finally, we use the neural-fitted TD-leaf algorithm to learn more effectively when look-ahead search is performed by the game-playing program. Our extensive experimental study clearly indicates that the proposed method outperforms linear networks and fully connected neural networks or evaluation functions evolved with evolutionary algorithms.

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