Array-Based Monte Carlo Tree Search
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908507505164288 |
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| author | Ragan, James Hadaegh, Fred Y. Chung, Soon-Jo |
| author_facet | Ragan, James Hadaegh, Fred Y. Chung, Soon-Jo |
| contents | Monte Carlo Tree Search is a popular method for solving decision making problems. Faster implementations allow for more simulations within the same wall clock time, directly improving search performance. To this end, we present an alternative array-based implementation of the classic Upper Confidence bounds applied to Trees algorithm. Our method preserves the logic of the original algorithm, but eliminates the need for branch prediction, enabling faster performance on pipelined processors, and up to a factor of 2.8 times better scaling with search depth in our numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Array-Based Monte Carlo Tree Search Ragan, James Hadaegh, Fred Y. Chung, Soon-Jo Artificial Intelligence Systems and Control Monte Carlo Tree Search is a popular method for solving decision making problems. Faster implementations allow for more simulations within the same wall clock time, directly improving search performance. To this end, we present an alternative array-based implementation of the classic Upper Confidence bounds applied to Trees algorithm. Our method preserves the logic of the original algorithm, but eliminates the need for branch prediction, enabling faster performance on pipelined processors, and up to a factor of 2.8 times better scaling with search depth in our numerical simulations. |
| title | Array-Based Monte Carlo Tree Search |
| topic | Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2508.20140 |