Learning Optimal Search Strategies
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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.
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References · 8
- 2020Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizoncited by 31
- 2021Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controlscited by 21
- 2021Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex modelscited by 18
- 2021Learning to reflect: A unifying approach for data-driven stochastic control strategiescited by 5
- 2023Statistical Learning with Sublinear Regret of Propagator Modelscited by 5
- 2023Data-driven rules for multidimensional reflection problemscited by 4
- 2023Data-driven optimal stopping: A pure exploration analysiscited by 3
- 2024Learning to steer with Brownian noisecited by 1
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