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

A new approach to the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms

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

1 Author(s)
Papadimitriou, Georgios I. ; Dept. of Comput. Eng., Patras Univ., Greece

A new class of learning automata is introduced. The new automata use a stochastic estimator and are able to operate in nonstationary environments with high accuracy and a high adaptation rate. According to the stochastic estimator scheme, the estimates of the mean rewards of actions are computed stochastically. So, they are not strictly dependent on the environmental responses. The dependence between the stochastic estimates and the deterministic estimator's contents is more relaxed when the latter are old and probably invalid. In this way, actions that have not been selected recently have the opportunity to be estimated as “optimal”, to increase their choice probability, and, consequently, to be selected. Thus, the estimator is always recently updated and consequently is able to be adapted to environmental changes. The performance of the Stochastic Estimator Learning Automaton (SELA) is superior to the previous well-known S-model ergodic schemes. Furthermore, it is proved that SELA is absolutely expedient in every stationary S-model random environment

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:6 ,  Issue: 4 )

Date of Publication:

Aug 1994

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.