Introduction
Multi-agent reinforcement learning (MARL) algorithms gradually learn good (ideally optimal) strategies with respect to long-term goals through trial-and-error interactions with both the opponent and the unknown dynamic environment.
The Stochastic Game (SG), together with MARL can address the environmental dynamics in security games in a systematic manner.
Reference
[1] A comprehensive survey of multi-agent reinforcement learning, by L. Busoniu, R. Babuska, and B. De Schutter, in IEEE Trans. Syst., Man, Cybern. C, 2008
[2] Improving Learning and Adaptation in Security Games by Exploiting Information Asymmetry, by Xiaofan He. Huaiyu Dai and Peng Ning, in INFOCOM 2015