The Influence of the Size of the Search Space on Learning to Play Chess using Deep Reinforcement Learning Algorithms

Bachelor thesis (2021)

Authors

A. Hakim Zakuto Electrical Engineering, Mathematics and Computer Science

Contributors

M.M. de Weerdt Algorithmics - EEMCS (supervisor 1)
G. Neustroev Algorithmics - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Aksel Hakim Zakuto
Rinforcement Learning Chess Search Space
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Published Date

28-06-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The current state-of-the-art solutions for playing Chess, are created using deep reinforcement learning. AlphaZero, the current world champion, uses ’policy networks’ and ’value network’ for selecting moves and evaluating positions respectively. However, the training of these networks are done using reinforcement learning from games of selfplay. There are many factors which determine the learning speed of reinforcement learning algorithms, where the size of the search space is a main one. In this research, we have tried to see the effect of the size of the search space on the time it takes the reinforcement learning agent to learn.