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Brain Computation as Hierarchical Abstraction$
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Dana H. Ballard

Print publication date: 2015

Print ISBN-13: 9780262028615

Published to MIT Press Scholarship Online: September 2015

DOI: 10.7551/mitpress/9780262028615.001.0001

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Programs via Reinforcement

Programs via Reinforcement

Chapter:
(p.163) 5 Programs via Reinforcement
Source:
Brain Computation as Hierarchical Abstraction
Author(s):

Dana H. Ballard

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262028615.003.0005

The learning of cognitive programs faces many technical difficulties, but the most important is the valuation of programs that have delayed rewards. The algorithms of Reinforcement Learning that the brain uses tackle this head-on are logically situated in the Basal Ganglia, which represent the abstract sequential components of motor and cognitive plans. Such sequences are evaluated in terms of their expected reward and risk, which in turn are coded, by using the neurotransmitters dopamine and serotonin respectively, which serves as a common evaluative currency. Reinforcement learning algorithms learn by adjusting deviations in expected reward, a signal, which can also be used to program the Cortex’s memory representations. Tesauro’s use of reinforcement to learn the game of Backgammon provides a superb example of the putative integration of the process between the two forebrain subsystems.

Keywords:   Cognitive Programs, Reinforcement Learning, Basal Ganglia, Forebrain

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