Hybrid Learning in Games with applications to Network Security

In this talk, we consider a class of N-person nonzero-sum stochastic games with incomplete information. We develop fully distributed reinforcement learning algorithms, which require for each player a minimal amount of information regarding the other players. We propose heterogeneous and hybrid learning mechanisms that allow players to change their learning schemes at each time based on their rationality and preferences. We apply our results to a class of security games in which the attacker and the defender adopt different learning schemes and update their strategies at random times.