Abstract
Two approaches to control policy synthesis for unknown systems are investigated. An indirect approach is based on adaptive identification of a neural network model in the NAR form (nonlinear autoregresion model) followed by application of the dynamic programming to this model. A direct approach consists of Q-learning with the use of a lookup table. Both methods were applied to optimization of a stock portfolio problem and tested on Warsaw Stock Exchange data.
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© 2003 Springer-Verlag Berlin Heidelberg
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Chrobak, J., Pacut, A. (2003). Dynamic Programming with NAR Model versus Q-learning — Case Study. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_113
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_113
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
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