  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.
