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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Yang, S.X., Meng, M., Yuan, Y.: A Biological Inspired Neural Network Approach to Realtime Collision-free Motion Planning of a Nonholonomic Car-like Robot. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 239–244 (2000)
Xiao, N.-F., Nahavandi, S.: A Reinforcement Learning Approach for Robot Control in an Unknown Environment. In: IEEE ICIT, Bangkok, Thailand, vol. 2, pp. 1096–1099 (2002)
Fan, J., Wu, G.F.: Reinforcement Learning and ART2 Neural Network Based Collision Avoidance System of Mobile Robot. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 35–40. Springer, Heidelberg (2004)
Chen, C.T., Quinn, R.D., Ritzmann, R.E.: A Crash Avoidance System Based Upon the Cockroach Escape Response Circuit. In: Proceeding of the IEEE International Conference on Robotics and Automation, pp. 2007–2012 (1997)
Whitehead, S.D., Sutton, R.S., Ballard, D.H.: Advances in reinforcement learning and their implications for intelligent control. Intelligent Control. In: Proceedings of the 5th IEEE International Symposium on Intelligent Control, vol. 2, pp. 1289–1297 (1990)
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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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