  Green security domains feature defenders who plan patrols in the face of
uncertainty about the adversarial behavior of poachers, illegal loggers, and
illegal fishers. Importantly, the deterrence effect of patrols on adversaries'
future behavior makes patrol planning a sequential decision-making problem.
Therefore, we focus on robust sequential patrol planning for green security
following the minimax regret criterion, which has not been considered in the
literature. We formulate the problem as a game between the defender and nature
who controls the parameter values of the adversarial behavior and design an
algorithm MIRROR to find a robust policy. MIRROR uses two reinforcement
learning-based oracles and solves a restricted game considering limited
defender strategies and parameter values. We evaluate MIRROR on real-world
poaching data.
