Abstract
This paper proposes a Foremost-Policy Reinforcement Learning based ART2 neural network (FPRL-ART2) and its learning algorithm. For real time learning, we select the first awarded behavior based on current state in the Foremost-Policy Reinforcement Learning (FPRL) in stead of the optimal behavior in 1-step Q-Learning. The paper also gives the algorithm of FPRL and integrates it with ART2 neural network. ART2 is used for storing the classified pattern and the stored weights of classified pattern is increased or decreased by reinforcement learning. FPRL-ART2 is successfully used in collision avoidance of mobile robot and the simulation experiment indicates that collision times between robot and obstacle are decreased effectively. FPRL-ART2 makes favorable effect against collision avoidance.
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Fan, J., Wu, G. (2005). A Foremost-Policy Reinforcement Learning Based ART2 Neural Network and Its Learning Algorithm. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_101
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DOI: https://doi.org/10.1007/11427391_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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