Learning Optimal Search Strategies
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915830306963456 |
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| author | Ankirchner, Stefan Thiel, Maximilian Philipp |
| author_facet | Ankirchner, Stefan Thiel, Maximilian Philipp |
| contents | We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a threshold-type stopping rule characterized by an indifference position. We propose an algorithm that learns this threshold by estimating the integrated jump intensity rather than the intensity function itself. We show that our algorithm achieves a logarithmic regret growth, uniformly over a broad class of environments. Moreover, we prove a logarithmic minimax regret lower bound, establishing the growth optimality of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02356 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Optimal Search Strategies Ankirchner, Stefan Thiel, Maximilian Philipp Machine Learning Probability We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a threshold-type stopping rule characterized by an indifference position. We propose an algorithm that learns this threshold by estimating the integrated jump intensity rather than the intensity function itself. We show that our algorithm achieves a logarithmic regret growth, uniformly over a broad class of environments. Moreover, we prove a logarithmic minimax regret lower bound, establishing the growth optimality of the proposed approach. |
| title | Learning Optimal Search Strategies |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2603.02356 |