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Reinforcement

About: Reinforcement is a research topic. Over the lifetime, 9207 publications have been published within this topic receiving 265106 citations. The topic is also known as: Reinforcement & Reinforcement, Psychology.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the effects of reward or reinforcement on preceding behavior depend in part on whether the person perceives the reward as contingent on his own behavior or independent of it, and individuals may also differ in generalized expectancies for internal versus external control of reinforcement.
Abstract: The effects of reward or reinforcement on preceding behavior depend in part on whether the person perceives the reward as contingent on his own behavior or independent of it. Acquisition and performance differ in situations perceived as determined by skill versus chance. Persons may also differ in generalized expectancies for internal versus external control of reinforcement. This report summarizes several experiments which define group differences in behavior when Ss perceive reinforcement as contingent on their behavior versus chance or experimenter control. The report also describes the development of tests of individual differences in a generalized belief in internal-external control and provides reliability, discriminant validity and normative data for 1 test, along with a description of the results of several studies of construct validity.

21,451 citations

Posted Content
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

5,970 citations

Journal ArticleDOI
01 Aug 2006-Carbon
TL;DR: In this article, a review of the progress to date in the field of mechanical reinforcement of polymers using nanotubes is presented, and the most promising processing methods for mechanical reinforcement are discussed.

3,770 citations

Book
01 Jan 1957

3,148 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20241
20232,165
20223,914
2021281
2020253