Deep learning and computer chess (part 2)

The dominant approach to computer chess has typically been through the use of Minimax-based chess engines. In recent years, Monte Carlo Tree Search (MCTS) game engines have seen success, with the advent of AlphaZero and Leela Chess Zero. However, there is still much to explore regarding the us...

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Main Author: Lee, Zachary Varella Zheyu
Other Authors: He Ying
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/166646
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1666462023-05-12T15:36:39Z Deep learning and computer chess (part 2) Lee, Zachary Varella Zheyu, He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Engineering::Computer science and engineering The dominant approach to computer chess has typically been through the use of Minimax-based chess engines. In recent years, Monte Carlo Tree Search (MCTS) game engines have seen success, with the advent of AlphaZero and Leela Chess Zero. However, there is still much to explore regarding the use of MCTS in the domain of chess. This paper evaluates the efficacy of an MCTS-based engine in the area of chess. On top of the base MCTS engine, several enhancements were proposed and implemented, including early playout termination, progressive bias, progressive unpruning, decisive moves, epsilon-greedy search, score bounded Monte-Carlo tree search, and root parallelization. Each enhancement was implemented in stages, and the performance of the enhancement was measured by comparing it to the model from the previous stage. It was determined that early playout termination, progressive unpruning, score bounded search, and root parallelization were effective in improving the playing strength of the engine. However, decisive moves and epsilon-greedy search negatively impacted the engine’s performance. From the results, it appears that it is possible to adapt an MCTS model to the realm of chess through the aid of several enhancements such that can compete with the traditional Minimax approach, with much room for improvement available. Bachelor of Engineering (Computer Science) 2023-05-08T08:09:46Z 2023-05-08T08:09:46Z 2023 Final Year Project (FYP) Lee, Z. V. Z. (2023). Deep learning and computer chess (part 2). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166646 https://hdl.handle.net/10356/166646 en SCSE22-0133 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 Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Lee, Zachary Varella Zheyu,
Deep learning and computer chess (part 2)
description The dominant approach to computer chess has typically been through the use of Minimax-based chess engines. In recent years, Monte Carlo Tree Search (MCTS) game engines have seen success, with the advent of AlphaZero and Leela Chess Zero. However, there is still much to explore regarding the use of MCTS in the domain of chess. This paper evaluates the efficacy of an MCTS-based engine in the area of chess. On top of the base MCTS engine, several enhancements were proposed and implemented, including early playout termination, progressive bias, progressive unpruning, decisive moves, epsilon-greedy search, score bounded Monte-Carlo tree search, and root parallelization. Each enhancement was implemented in stages, and the performance of the enhancement was measured by comparing it to the model from the previous stage. It was determined that early playout termination, progressive unpruning, score bounded search, and root parallelization were effective in improving the playing strength of the engine. However, decisive moves and epsilon-greedy search negatively impacted the engine’s performance. From the results, it appears that it is possible to adapt an MCTS model to the realm of chess through the aid of several enhancements such that can compete with the traditional Minimax approach, with much room for improvement available.
author2 He Ying
author_facet He Ying
Lee, Zachary Varella Zheyu,
format Final Year Project
author Lee, Zachary Varella Zheyu,
author_sort Lee, Zachary Varella Zheyu,
title Deep learning and computer chess (part 2)
title_short Deep learning and computer chess (part 2)
title_full Deep learning and computer chess (part 2)
title_fullStr Deep learning and computer chess (part 2)
title_full_unstemmed Deep learning and computer chess (part 2)
title_sort deep learning and computer chess (part 2)
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166646
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