Real-Time IDS Using Reinforcement Learning

被引：0

作者：

Sagha, Hesam ^{[1
]}

Shouraki, Saeed Bagheri ^{[2
]}

Khasteh, Hosein ^{[1
]}

Dehghani, Mahdi ^{[1
]}

机构：

[1] Sharif Univ Technol, Dept Comp Engn, Tehran, Iran

[2] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

2008 INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL II, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/IITA.2008.512

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：593 / +

页数：2

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