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Journal Article

Bayesian reinforcement learning: A basic overview

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Dayan,  P       
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Kang, P., Tobler, P., & Dayan, P. (2024). Bayesian reinforcement learning: A basic overview. Neurobiology of Learning and Memory, 211: 107924. doi:10.1016/j.nlm.2024.107924.


Cite as: https://hdl.handle.net/21.11116/0000-000F-2052-0
Abstract
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.