Publication Date
Fall 2018
Advisor(s) - Committee Chair
Dr. Uta Ziegler (Director), Dr. James Gary, and Dr. Zhonghang Xia
Degree Program
School of Engineering and Applied Sciences
Degree Type
Master of Science
Abstract
In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) led to a player which beat all its competitors.
Disciplines
Other Computer Sciences | Robotics | Theory and Algorithms
Recommended Citation
Prince, Jared, "Exploring the Effect of Different Numbers of Convolutional Filters and Training Loops on the Performance of AlphaZero" (2018). Masters Theses & Specialist Projects. Paper 3087.
https://digitalcommons.wku.edu/theses/3087