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What is So Interesting About Reinforcement Learning?

Computational Neuroscience seminar by Andrew Barto, University of Massachusetts Amherst

Hosted by van Vreeswijk Theoretical Neuroscience Seminar

Wednesday 11:00 New York (GMT-4)

Recording available

Recording

Abstract

This talk aims to answer these questions along four dimensions.  First is history. RL was the basis of AI long before the term AI was introduced in 1956. The first machine learning (ML) systems were based on RL even before digital computers existed. Despite notable early successes of ML based on RL, RL essentially disappeared from ML until relatively recently. A second reason for renewed interest in RL is the clarification of some misunderstandings that have been prevalent in the ML community. A third, and most important, reason for this resurgence is that new, or rediscovered, algorithms and connections to well developed mathematical and engineering methods have been worked out. Finally, a fourth reason for the renewed interest in RL is its strong links to animal reward systems, in particular, to the role that dopamine plays in motivation and learning.

VVTNS Sixth Season Opening Lecture.

Topics

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engineering methodstheoretical neuroscience

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