Real-Time IDS Using Reinforcement Learning
Hesam Sagha, Saeed Bagheri Shouraki, Hosein Khasteh, Mahdi Dehghani · 2008
In this paper we proposed a new real-time learning method. The engine of this method is a fuzzy-modeling technique which is called ink drop spread (IDS). IDS method has good convergence and is very simple and away from complex formula. The proposed method uses a reinforcement learning approach by an actor-critic system similar to generalized approximate reasoning based intelligent control (GARIC) structure to adapt the IDS by delayed reinforcement signals. Our system uses temporal difference (TD) learning to model the behavior of useful actions of a control system. It is shown that the system can adapt itself, commencing with random actions.