# Learning to reset in target search problems

arXiv: https://arxiv.org/abs/2503.11330 · HTML: https://mlsanity.com/paper/2503.11330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 3 of 3)

- [Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)](https://mlsanity.com/paper/2002.08136/) (2020)
- [Optimal foraging strategies can be learned](https://mlsanity.com/paper/2303.06050/) (2023)
- [Optimal active particle navigation meets machine learning](https://mlsanity.com/paper/2303.05558/) (2023)

## Built on by (most cited first, 2 of 2)

- [Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search](https://mlsanity.com/paper/2603.16842/) (2026)
- [Emergent aggregation from collective foraging](https://mlsanity.com/paper/2608.28046/) (2026)

## Abstract (arXiv)

Target search problems are central to a wide range of fields, from biological foraging to the optimization algorithms. Recently, the ability to reset the search has been shown to significantly improve the searcher's efficiency. However, the optimal resetting strategy depends on the specific properties of the search problem and can often be challenging to determine. In this work, we propose a reinforcement learning (RL)-based framework to train agents capable of optimizing their search efficiency in environments by learning how to reset. First, we validate the approach in a well-established benchmark: the Brownian search with resetting. There, RL agents consistently recover strategies closely resembling the sharp resetting distribution, known to be optimal in this scenario. We then extend the framework by allowing agents to control not only when to reset, but also their spatial dynamics through turning actions. In this more complex setting, the agents discover strategies that adapt both resetting and turning to the properties of the environment, outperforming the proposed benchmarks. These results demonstrate how reinforcement learning can serve both as an optimization tool and a mechanism for uncovering new, interpretable strategies in stochastic search processes with resetting.