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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© 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
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