By Topic

Genetic reinforcement learning for neural networks

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

4 Author(s)
Dominic, S. ; Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA ; Das, R. ; Whitley, D. ; Anderson, C.

It is pointed out that the genetic algorithms which have been shown to yield good performance for neural network weight optimization are really genetic hill-climbers, with a strong reliance on mutation rather than hyperplane sampling. Neural control problems are more appropriate for these genetic hill-climbers than supervised learning applications because in reinforcement learning applications gradient information is not directly available. Genetic reinforcement learning produces competitive results with the adaptive heuristic critic method, another reinforcement learning paradigm for neural networks that employs temporal difference methods. The genetic hill-climbing algorithm appears to be robust over a wide range of learning conditions

Published in:

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on  (Volume:ii )

Date of Conference:

8-14 Jul 1991