# Learning Optimal Search Strategies

arXiv: https://arxiv.org/abs/2603.02356 · HTML: https://mlsanity.com/paper/2603.02356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon](https://mlsanity.com/paper/2006.15316/) (2020)
- [Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls](https://mlsanity.com/paper/2104.09311/) (2021)
- [Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models](https://mlsanity.com/paper/2112.10264/) (2021)
- [Learning to reflect: A unifying approach for data-driven stochastic control strategies](https://mlsanity.com/paper/2104.11496/) (2021)
- [Statistical Learning with Sublinear Regret of Propagator Models](https://mlsanity.com/paper/2301.05157/) (2023)
- [Data-driven rules for multidimensional reflection problems](https://mlsanity.com/paper/2311.06639/) (2023)
- [Data-driven optimal stopping: A pure exploration analysis](https://mlsanity.com/paper/2312.05880/) (2023)
- [Learning to steer with Brownian noise](https://mlsanity.com/paper/2410.03221/) (2024)

## Abstract (arXiv)

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.