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Automatic Car Parking: A Reinforcement Learning Approach

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2686))

Abstract

The automatic parking of a car-like robot is the problem considered in this paper to evaluate the role played by formal representations and models in neural-based controllers. First, a model-free control scheme is introduced. The respective control actions are sensory-based and consist of a dynamic, neural-based process in which the neurocontroller optimizes ad hoc performance functions. Afterwards, a model-based neurocontroller that builds without supervised a formal representation of its interaction with the environment is proposed. The resulting model is eventually utilized to generate the control actions. Simulated experimentation has shown that there is an improvement in robot behavior when a model is used, at the cost of higher complexity and computational load.

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© 2003 Springer-Verlag Berlin Heidelberg

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Maravall, D., Patricio, M.Á., de Lope, J. (2003). Automatic Car Parking: A Reinforcement Learning Approach. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_28

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  • DOI: https://doi.org/10.1007/3-540-44868-3_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40210-7

  • Online ISBN: 978-3-540-44868-6

  • eBook Packages: Springer Book Archive

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