# Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark

arXiv: https://arxiv.org/abs/2302.00706 · HTML: https://mlsanity.com/paper/2302.00706/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2023.

## Built on by (most cited first, 6 of 6)

- [Q-learning with temporal memory to navigate turbulence](https://mlsanity.com/paper/2404.17495/) (2024)
- [Guiding drones by information gain](https://mlsanity.com/paper/2401.03947/) (2024)
- [A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows](https://mlsanity.com/paper/2505.05525/) (2025)
- [Multi-source Plume Tracing via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.08825/) (2025)
- [Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery](https://mlsanity.com/paper/2605.15938/) (2026)
- [Emergence of a Flow-Assisted Casting Strategy for Olfactory Navigation via Memory-Augmented Reinforcement Learning](https://mlsanity.com/paper/2605.18881/) (2026)

## Abstract (arXiv)

The olfactory search POMDP (partially observable Markov decision process) is a sequential decision-making problem designed to mimic the task faced by insects searching for a source of odor in turbulence, and its solutions have applications to sniffer robots. As exact solutions are out of reach, the challenge consists in finding the best possible approximate solutions while keeping the computational cost reasonable. We provide a quantitative benchmarking of a solver based on deep reinforcement learning against traditional POMDP approximate solvers. We show that deep reinforcement learning is a competitive alternative to standard methods, in particular to generate lightweight policies suitable for robots.