Markov Security Games : Learning in Spatial Security Problems

In this paper we present a preliminary investigation of modelling spatial aspects of security games within the context of Markov games. Reinforcement learning is a powerful tool for adaptation in unknown environments, however the basic singleagent RL algorithms are unfit to be applied in adversarial scenarios. Therefore, we profit from Adversarial Multi-Armed Bandit (AMAB) methods which are designed for such situations. Based on temporal difference methods we derive two new multiagent algorithms using AMAB methods for spatial two-player non-cooperative security games.

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