OlfactorySearchRL: A Measured-Plume Benchmark for Learning to Navigate by Smell

Olfactory navigation requires an agent to infer a hidden source from intermittent local measurements while choosing where to sample next. Progress in learning this behaviour depends on environments that combine realistic chemical structure with repeatable evaluation. We introduce OLFACTORYSEARCHRL (OSRL), a modular toolbox and reference benchmark that turns experimentally measured turbulent concentration fields into interactive learning environments. OSRL separates plume replay, sensing, observation processing, and control, so researchers can compare policies and representations on a common physical record. A fixed 135-episode track is used to compare navigation policies, where here we evaluate Infotaxis, PMFS, and RNN-PPO on three held-out recordings. We additionally retrain the RNN-PPO policy with a dynamic gas sensor model, and showcase successful robotic deployment. In particular, we demonstrate that a robotic agent equipped with chemoresistive gas sensors successfully reaches the source region in complex turbulent flow in 29 of 30 physical trials, without further policy fine-tuning. We release the toolbox and a reproduction package containing configurations, checkpoints, evaluation records, and analysis scripts. OSRL provides an experimental foundation for studying active sensing and state estimation in turbulent chemical environments.