Reinforcement Learning


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.


[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

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