Using Monte Carlo tree search and machine learning to learn a heuristic function

Karin Frlic (2019) Using Monte Carlo tree search and machine learning to learn a heuristic function. EngD thesis.

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Abstract

Minimax algorithm is one of the most widely used algorithms for playing two-player games. It uses a heuristic function that estimates the benefits of reaching a given game state for both players. In this bachelor thesis we attempt to automatically construct that kind of a function for the game of Hex. Different models of supervised machine learning are trained on learning samples, generated by simulations of MCTS. As a result, the player that uses minimax with α-β and the learnt function performs worse than the player that uses pure MCTS. However, the player combining advantages of both players achieves better results than MCTS.

Item Type:

Thesis (EngD thesis)

Keywords:

Monte Carlo tree search, supervised machine learning, minimax algorithm, heuristic evaluation function, alpha-beta pruning, the game of Hex

Number of Pages:

Language of Content:

Slovenian

Mentor / Comentors:

Name and Surname	ID	Function
doc. dr. Aleksander Sadikov	934	Mentor

Link to COBISS:

http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1538106051)

Institution:

University of Ljubljana

Department:

Faculty of Computer and Information Science

Item ID:

4337

Date Deposited:

15 Jan 2019 11:26

Last Modified:

24 Jan 2019 11:08

URI:

http://eprints.fri.uni-lj.si/id/eprint/4337

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