Deep learning and chess

Deep learning and chess

This paper investigates the application of neural networks and Monte Carlo Tree Search (MCTS) for the development of a chess-playing agent. Our experiments include both full-game and isolated-position testing environments. Through rigorous evaluation, we show that the integration of neural networ...

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Bibliographic Details
Main Author: Nguyen, Gia Khanh
Other Authors: He Ying
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Chess
Deep learning
Online Access:https://hdl.handle.net/10356/181411
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-181411
record_format dspace
spelling sg-ntu-dr.10356-1814112024-12-02T02:57:13Z Deep learning and chess Nguyen, Gia Khanh He Ying College of Computing and Data Science YHe@ntu.edu.sg Computer and Information Science Chess Deep learning This paper investigates the application of neural networks and Monte Carlo Tree Search (MCTS) for the development of a chess-playing agent. Our experiments include both full-game and isolated-position testing environments. Through rigorous evaluation, we show that the integration of neural networks with MCTS greatly improves the agent’s decision-making and performance compared to traditional methods. Bachelor's degree 2024-12-02T02:57:13Z 2024-12-02T02:57:13Z 2024 Final Year Project (FYP) Nguyen, G. K. (2024). Deep learning and chess. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181411 https://hdl.handle.net/10356/181411 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Chess
Deep learning
spellingShingle Computer and Information Science
Chess
Deep learning
Nguyen, Gia Khanh
Deep learning and chess
description This paper investigates the application of neural networks and Monte Carlo Tree Search (MCTS) for the development of a chess-playing agent. Our experiments include both full-game and isolated-position testing environments. Through rigorous evaluation, we show that the integration of neural networks with MCTS greatly improves the agent’s decision-making and performance compared to traditional methods.
author2 He Ying
author_facet He Ying
Nguyen, Gia Khanh
format Final Year Project
author Nguyen, Gia Khanh
author_sort Nguyen, Gia Khanh
title Deep learning and chess
title_short Deep learning and chess
title_full Deep learning and chess
title_fullStr Deep learning and chess
title_full_unstemmed Deep learning and chess
title_sort deep learning and chess
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181411
_version_ 1819112971491606528

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