Model-Based Reinforcement Learning under Periodical Observability

The uncertainty induced by unknown attacker locations is one of the problems in deploying AI methods to security domains. We study a model with partial observability of the attacker location and propose a novel reinforcement learning method using partial information about attacker behaviour coming from the system. This method is based on deriving beliefs about underlying states using Bayesian inference. These beliefs are then used in the QMDP algorithm. We particularly design the algorithm for spatial security games, where the defender faces intelligent and adversarial opponents.