Abstract
This paper proposes the use of behavior-based control architecture and investigates on some techniques inspired by Nature- a combination of reinforcement and supervised learning algorithms to accomplish the sub-goals of a mission of building adaptive controller. The approach iteratively improves its control strategies by exploiting only relevant parts of action and is able to learn completely in on-line mode. To illustrate this, it has been applied to non-linear, non-stationary control task: Cart-Pole balancing. The results demonstrate that our hybrid approach is adaptable and can significantly improve the performance of TD methods while speed up learning process.
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References
Barto, A., Sutton, R., Anderson, C.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics SMC-13, 834–846 (1983)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Hassab Elgawi, O.: Architecture of Knowledge-Based Function Approximator. In: Bramer, M. (ed.) Research and Development in Intelligent Systems XXV. Proc. 28th SGAI Int’l. Conference on Artificial Intelligent (AI 2008), Springer, London (2008)
Hassab Elgawi, O.: A hybrid Architecture for Function Approximation. In: Proc. 6th IEEE Int’l. Conf on Industrial Informatics (INDIN 2008), pp. 1103–1108 (2008)
Hassab Elgawi, O.: Online Random Forests based on CorrFS and CorrBE. In: Proc.IEEE workshop on online classification, CVPR, pp. 1–7 (2008)
Keller, P.W., Mannor, S., Precup, D.: Automatic basis function construction for approximate dynamic programming and reinforcement learning. In: Proc. of the 23rd international conference on Machine learning, ICML, pp. 449–456 (2006)
Sutton, R.: Learning to predict by the method of temporal differences. Machine Learning 3(1), 9–44 (1988)
Sutton, R.: Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. Advances in Neural Information Processing Systems 8, 1038–1044 (1996)
Sutton, R., McAllester, D., Singh, S., Mansour, Y.: Policy Gradient Methods for Reinforcement Learning with Function Approximation. Advances in Neural Information Processing Systems 12, 1057–1063 (2000)
Peter, S., Sutton, R., Kuhlmann, G.: Reinforcement Learning for RoboCup-Soccer Keepaway. Adaptive Behavior 13(3), 165–188 (2005)
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Osman, H.E. (2009). Architecture of Behavior-Based Function Approximator for Adaptive Control. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_13
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DOI: https://doi.org/10.1007/978-3-642-03040-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
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