Deep Learning and the Game of Checkers

  • Jan Popic Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
  • Borko Boskovic Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
  • Janez Brest Institute of Computer Science, Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia
Keywords: Artificial Intelligence, Deep Learning, Convolutional Neural Network, Reinforcement Learning, Checkers

Abstract

In this paper we present an approach which given only a set of rules is able to learn to play the game of Checkers. We utilize neural networks and reinforced learning combined with Monte Carlo Tree Search and alpha-beta pruning. Any human influence or knowledge is removed by generating needed data, for training neural network, using self-play. After a certain number of finished games, we initialize the training and transfer better neural network version to next iteration. We compare different obtained versions of neural networks and their progress in playing the game of Checkers. Every new version of neural network represented a better player.

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Published
2021-12-21
How to Cite
[1]
Popic, J., Boskovic, B. and Brest, J. 2021. Deep Learning and the Game of Checkers. MENDEL. 27, 2 (Dec. 2021), 1-6. DOI:https://doi.org/10.13164/mendel.2021.2.001.
Section
Articles