Grey Reinforcement Learning for Incomplete Information Processing

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• 作者：Chunlin Chen ; Daoyi Dong ; Zonghai Chen
• 刊名：Lecture Notes in Computer Science
• 出版年：2006
• 出版时间：2006
• 年：2006
• 卷：3959
• 期：1
• 页码：pp.399-407
• 全文大小：436 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

New representation and computation mechanisms are key approaches for learning problems with incomplete information or in large probabilistic environments. In this paper, traditional reinforcement learning (RL) methods are combined with grey theory and a novel grey reinforcement learning (GRL) framework is proposed to solve complex problems with incomplete information. Typical example of mobile robot navigation is given out to evaluate the performance and practicability of GRL. Related issues are also briefly discussed.