Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911839267323904 |
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| author | Cazenave, Tristan |
| author_facet | Cazenave, Tristan |
| contents | Monte Carlo Tree Search and Monte Carlo Search have good results for many combinatorial problems. In this paper we propose to use Monte Carlo Search to design mathematical expressions that are used as exploration terms for Monte Carlo Tree Search algorithms. The optimized Monte Carlo Tree Search algorithms are PUCT and SHUSS. We automatically design the PUCT and the SHUSS root exploration terms. For small search budgets of 32 evaluations the discovered root exploration terms make both algorithms competitive with usual PUCT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms Cazenave, Tristan Artificial Intelligence Monte Carlo Tree Search and Monte Carlo Search have good results for many combinatorial problems. In this paper we propose to use Monte Carlo Search to design mathematical expressions that are used as exploration terms for Monte Carlo Tree Search algorithms. The optimized Monte Carlo Tree Search algorithms are PUCT and SHUSS. We automatically design the PUCT and the SHUSS root exploration terms. For small search budgets of 32 evaluations the discovered root exploration terms make both algorithms competitive with usual PUCT. |
| title | Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2404.09304 |