Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866909954868248576 |
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| author | Xiao, Junlin Darvariu, Victor-Alexandru Lacerda, Bruno Hawes, Nick |
| author_facet | Xiao, Junlin Darvariu, Victor-Alexandru Lacerda, Bruno Hawes, Nick |
| contents | Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired. In environments with continuous action spaces, how to best aggregate statistics from different threads is an important yet underexplored question. In this work, we introduce a method that uses Gaussian Process Regression to obtain value estimates for promising actions that were not trialed in the environment. We perform a systematic evaluation across 6 different domains, demonstrating that our approach outperforms existing aggregation strategies while requiring a modest increase in inference time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09727 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions Xiao, Junlin Darvariu, Victor-Alexandru Lacerda, Bruno Hawes, Nick Artificial Intelligence Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired. In environments with continuous action spaces, how to best aggregate statistics from different threads is an important yet underexplored question. In this work, we introduce a method that uses Gaussian Process Regression to obtain value estimates for promising actions that were not trialed in the environment. We perform a systematic evaluation across 6 different domains, demonstrating that our approach outperforms existing aggregation strategies while requiring a modest increase in inference time. |
| title | Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09727 |