Cart (Loading....) | Create Account
Close category search window

Reinforcement learning method based on semi-parametric regression model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.