First Advisor

Bruno Jedynak

Term of Graduation

Fall 2022

Date of Publication

11-2-2021

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Systems Science

Department

Systems Science

Language

English

Subjects

Artificial intelligence, Machine learning, Reinforcement learning

DOI

10.15760/etd.7738

Physical Description

1 online resource (vi, 95 pages)

Abstract

In this paper I will explain the AlphaGo family of algorithms starting from first principles and requiring little previous knowledge from the reader. The focus will be upon one of the more recent versions AlphaZero but I hope to explain the core principles that allowed these algorithms to be so successful. I will generally refer to AlphaZero as theses [sic] core set of principles and will make it clear when I am referring to a specific algorithm of the AlphaGo family. AlphaZero in short combines Monte Carlo Tree Search (MCTS) with Deep learning and self-play. We will see how these three concepts fit together and we will break down each of these pieces and look at examples to clarify understanding. I implemented a simplified version of the algorithm on TicTacToe and Connect4 and the code is available online as well as a simple web app that allows you to play against a trained agent.

https://github.com/befeltingu/VikingZero

https://github.com/befeltingu/VikingDashboard

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/36926

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