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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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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