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Cited by 446

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Adaptive Critics and the Basal Ganglia

Barto, Andrew G.·1994·Models of Information Processing in the Basal Ganglia
DOI 10.7551/mitpress/4708.003.0018
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Abstract

One of the most active areas of research in artificial intelligence is the study of learning methods by which “embedded agents ” can improve performance while acting in complex dynamic environments. An agent, or decision maker, is embedded in an environment when it receives information from, and acts on, that environment in an ongoing closed-loop interaction. An embedded agent has to make decisions under time pressure and uncertainty and has to learn without the help of an ever-present knowledgeable teacher. Although the novelty of this emphasis may be inconspicuous to a biologist, animals being the prototypical embedded agents, this emphasis is a significant departure from the more traditional focus in artificial intelligence on reasoning within circumscribed domains removed from the flow of real-world events. One consequence of the embedded agent view is the increasing interest in the learning paradigm called reinforcement learning (RL). Unlike the more widely studied supervised learning systems, which learn from a set of examples of correct input/output behavior, RL systems adjust their behavior with the goal of maximizing the frequency and/or magnitude of the reinforcing events they encounter over time. While the core ideas of modern RL come from theories of animal classical and instrumental

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Metadata

Authors
Barto, A. G.
Venue
Models of Information Processing in the Basal Ganglia
Publisher
The MIT Press
Published
1994-11-07
Pages
215-232
DOI
10.7551/mitpress/4708.003.0018
OpenAlex
W2153562927
Cited by
446
References
2
FWCI
8.14
Type
book chapter
Language
en
Open access
closed

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