Robin A. Heinonen
5 papers 51 citations h-index 2 2022–2026
Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark
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...
Optimal policies for Bayesian olfactory search in turbulent flows
In many practical scenarios, a flying insect must search for the source of an emitted cue which is advected by the atmospheric wind. On the macroscopic scales of interest, turbulence tends to mix...
Clock-state olfactory search in turbulent flows using Q-learning: The geometry of plume recovery
Finding an odor source in a turbulent flow requires effectively leveraging the history of olfactory observations into a robust navigation strategy. In this work, we use tabular Q-learning to train...
Olfactory pursuit: catching a moving odor source in complex flows
Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncertainty, and a fundamental challenge for, e.g.,...
Asymptotics of solutions to the linear search problem
The exact leading asymptotics of solutions to the symmetric linear search problem are obtained for any positive probability density on the real line with a monotonic, sufficiently regular tail. A...