Vague neural network based reinforcement learning control system for inverted pendulum

  • Authors:

  • Yibiao Zhao;Siwei Luo;Liang Wang;Aidong Ma;Rui Fang

  • Affiliations:

  • Beijing Jiaotong University, School of Traffic and Transportation, Beijing, P.R. China;Beijing Jiaotong University, School of Computer and Information Technology, Beijing, P.R. China;Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, P.R. China;Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, P.R. China;Beijing Jiaotong University, School of Computer and Information Technology, Beijing, P.R. China

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
  • Year:
  • 2006

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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.