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Reinforcement learning method based on semi-parametric regression model

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3 Author(s)
Yuhu Cheng ; Sch. of Inf. & Electr. Eng., China Univ. of Min. & Technol., Xuzhou, China ; Xuesong Wang ; Xilan Tian

In order to make full use of the advantages of both parametric and non-parametric models simultaneously, a kind of semi-parametric support vector machine (SVM) was proposed by combining a non-parametric SVM model and a parametric linear basis function model. The semi-parametric SVM was used to estimate the Q values of continuous-state-discontinuous-action pairs in an on-line manner so as to generalize a standard Q learning method to continuous state spaces. Simulation results concerning the balancing control problem of an inverted pendulum show that the proposed Q learning method has good adaptability for changes of system parameters and initial states, which provides a new approach to solve the generalization problem of continuous space of reinforcement learning.

Published in:

Control and Decision Conference (CCDC), 2010 Chinese

Date of Conference:

26-28 May 2010

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