By Topic

Reinforcement learning is direct adaptive optimal control

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
$33 $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)
R. S. Sutton ; GTE Lab. Inc., Waltham, MA, USA ; A. G. Barto ; R. J. Williams

Neural network reinforcement learning methods are described and considered as a direct approach to adaptive optimal control of nonlinear systems. These methods have their roots in studies of animal learning and in early learning control work. An emerging deeper understanding of these methods is summarized that is obtained by viewing them as a synthesis of dynamic programming and stochastic approximation methods. The focus is on Q-learning systems, which maintain estimates of utilities for all state-action pairs and make use of these estimates to select actions. The use of hybrid direct/indirect methods is briefly discussed.<>

Published in:

IEEE Control Systems  (Volume:12 ,  Issue: 2 )