Markov Security Games: Learning in Spatial Security Problems
نویسندگان
چکیده
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.
منابع مشابه
Adversarial AI
In recent years AI research has had an increasing role in models and algorithms for security problems. Game theoretic models of security, and Stackelberg security games in particular, have received special attention, in part because these models and associated tools have seen actual deployment in homeland security and sustainability applications. Stackelberg security games have two prototypical...
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