Hybrid Learning in Stochastic Games and Its Application in Network Security
Quanyan Zhu, Hamidou Tembiné, Tamer Başar · 2012
We consider in this chapter a class of two-player nonzero-sum stochastic games with incomplete information, which is inspired by recent applications of game theory in network security. We develop fully distributed reinforcement learning algorithms, which require for each player a minimal amount of information regarding the other player. At each time, each player can be in an active mode or in a sleep mode. If