Grey Reinforcement Learning for Incomplete Information Processing

  • Chunlin Chen
  • Daoyi Dong
  • Zonghai Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3959)


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.


Mobile Robot Reinforcement Learning Markov Decision Process Grey Model Partially Observable Markov Decision Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chunlin Chen
    • 1
  • Daoyi Dong
    • 1
  • Zonghai Chen
    • 1
  1. 1.Department of AutomationUniversity of Science and Technology of ChinaHefei, AnhuiP.R. China

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