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Real-Time IDS using reinforcement learning

Sagha, H ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/IITA.2008.512
  3. Publisher: 2008
  4. Abstract:
  5. 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. © 2008 IEEE
  6. Keywords:
  7. Actor critics ; Approximate reasonings ; Ink drop spreads ; Modeling techniques ; Real-time learning ; Reinforcement learning approaches ; Reinforcement signals ; System use ; Temporal difference learning ; Education ; Information technology ; Reinforcement learning ; Reinforcement
  8. Source: 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, Shanghai, 21 December 2008 through 22 December 2008 ; Volume 2 , January , 2008 , Pages 593-597 ; 9780769534978 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4739833