By Topic

Online Support Vector Regression based value function approximation for Reinforcement Learning

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

4 Author(s)
Dong-Hyun Lee ; Robot. Program, KAIST, Daejeon, South Korea ; Vo Van Quang ; Sungho Jo ; Ju-Jang Lee

This paper proposes the online Support Vector Regression (SVR) based value function approximation method for Reinforcement Learning (RL). This approach conserves the Support Vector Machine (SVM)'s good property, the generalization which is a key issue of function approximation. Online SVR can do incremental learning and automatically track variation of environment with time-varying characteristics. Using the online SVR, we can obtain the fast and good estimation of value function and achieve RL objective efficiently. Throughout simulation tests, the feasibility and usefulness of the proposed approach is demonstrated by comparison with SARSA and Q-learning.

Published in:

Industrial Electronics, 2009. ISIE 2009. IEEE International Symposium on

Date of Conference:

5-8 July 2009