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Vague Neural Network Based Reinforcement Learning Control System for Inverted Pendulum

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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Abstract

Reinforcement learning is a class of model-free learning control method that can solve Markov decision problems. But it has some problems in applications, especially in MDPs of continuous state spaces. In this paper, based on the vague neural networks, we propose a Q-learning algorithm which is comprehensively considering the reward and punishment of the environment. Simulation results in cart-pole balancing problem illustrate the effectiveness of the proposed method.

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References

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Author information

Authors and Affiliations

  1. School of Traffic and Transportation, Beijing Jiaotong University, China

    Yibiao Zhao

  2. School of Computer and Information Technology, Beijing Jiaotong University, China

    Siwei Luo & Rui Fang

  3. School of Electronics and Information Engineering, Beijing Jiaotong University, 100044, Beijing, P.R. China

    Liang Wang & Aidong Ma

Authors
  1. Yibiao Zhao
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  2. Siwei Luo
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  3. Liang Wang
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  4. Aidong Ma
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  5. Rui Fang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhao, Y., Luo, S., Wang, L., Ma, A., Fang, R. (2006). Vague Neural Network Based Reinforcement Learning Control System for Inverted Pendulum. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_76

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  • DOI: https://doi.org/10.1007/11893295_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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