Learning Generalizable Patrolling Strategies through Domain Randomization of Attacker Behaviors

Graph-patrolling problems in the adversarial domain typically embed models and assumptions about how hostile events, from which an environment must be protected, are generated at a specific time and location. Relying upon such attacker models prevents algorithms from synthesizing strategies that can generalize in different settings, providing good performance under different and uncertain scenarios. In this paper, we propose a first method to deal with adversarial patrolling using a data driven approach. We cast the problem in an RL setting where the reward function is based on the ability to neutralize attacks that can follow an unknown strategy and that, hence, can be viewed as a black box component. We apply a policy gradient framework for optimizing action probabilities under such a reward model showing how effective patrolling strategies can be obtained from repeated attack-defense interactions between a patrolling agent and an attacker. Our results show that the data driven patroller can effectively provide protection against multiple, diverse attacker behaviors.