We have located links that may give you full text access.
Bayesian reinforcement learning: A basic overview.
Neurobiology of Learning and Memory 2024 April 4
We and other animals learn because there is some aspect of the world about which we are uncertain. This uncertainty arises from initial ignorance, and from changes in the world that we do not perfectly know; the uncertainty often becomes evident when our predictions about the world are found to be erroneous. The Rescorla-Wagner learning rule, which specifies one way that prediction errors can occasion learning, has been hugely influential as a characterization of Pavlovian conditioning and, through its equivalence to the delta rule in engineering, in a much wider class of learning problems. Here, we review the embedding of the Rescorla-Wagner rule in a Bayesian context that is precise about the link between uncertainty and learning, and thereby discuss extensions to such suggestions as the Kalman filter, structure learning, and beyond, that collectively encompass a wider range of uncertainties and accommodate a wider assortment of phenomena in conditioning.
Full text links
Related Resources
Trending Papers
Renin-Angiotensin-Aldosterone System: From History to Practice of a Secular Topic.International Journal of Molecular Sciences 2024 April 5
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app