Advanced Reinforcement Learning

Nathaniel D. Daw

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter reviews issues of current research in reinforcement learning theories and their neural substrates. We consider how the formal constructs of states, actions, and rewards that these theories describe can be understood to map onto counterparts experienced by biological organisms learning in the real world. In each case, this correspondence involves significant difficulties. However, elaborated theoretical accounts from computer science clarify, in each case, how to extend these theories to more realistic circumstances while still preserving the core prediction error-driven learning mechanism that has been prominent in neuroeconomic accounts.

Original languageEnglish (US)
Title of host publicationNeuroeconomics
Subtitle of host publicationDecision Making and the Brain: Second Edition
PublisherElsevier Inc.
Pages299-320
Number of pages22
ISBN (Print)9780124160088
DOIs
StatePublished - Sep 2013

Keywords

  • Dopamine
  • Hierarchical reinforcement learning
  • Reinforcement learning
  • Uncertainty

ASJC Scopus subject areas

  • Neuroscience(all)

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