  Pursuit-evasion is a multi-agent sequential decision problem wherein a group
of agents known as pursuers coordinate their traversal of a spatial domain to
locate an agent trying to evade them. Pursuit evasion problems arise in a
number of import application domains including defense and route planning.
Learning to optimally coordinate pursuer behaviors so as to minimize time to
capture of the evader is challenging because of a large action space and sparse
noisy state information; consequently, previous approaches have relied
primarily on heuristics. We propose a variant of Thompson Sampling for
pursuit-evasion that allows for the application of existing model-based
planning algorithms. This approach is general in that it allows for an
arbitrary number of pursuers, a general spatial domain, and the integration of
auxiliary information provided by informants. In a suite of simulation
experiments, Thompson Sampling for pursuit evasion significantly reduces
time-to-capture relative to competing algorithms.
