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Reinforcement

About: Reinforcement is a(n) research topic. Over the lifetime, 9207 publication(s) have been published within this topic receiving 265106 citation(s). The topic is also known as: Reinforcement & Reinforcement, Psychology.
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Journal ArticleDOI
Julian B. Rotter1Institutions (1)
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.

20,553 citations


Posted Content
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
Abstract: The superlative mechanical properties of carbon nanotubes make them the filler material of choice for composite reinforcement. In this paper we review the progress to date in the field of mechanical reinforcement of polymers using nanotubes. Initially, the basics of fibre reinforced composites are introduced and the prerequisites for successful reinforcement discussed. The effectiveness of different processing methods is compared and the state of the art demonstrated. In addition we discuss the levels of reinforcement that have actually been achieved. While the focus will be on enhancement of Young’s modulus we will also discuss enhancement of strength and toughness. Finally we compare and tabulate these results. This leads to a discussion of the most promising processing methods for mechanical reinforcement and the outlook for the future.

3,534 citations


Book
C. B. Ferster1, B. F. Skinner2Institutions (2)
01 Jan 1957-

3,044 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20226
2021197
2020244
2019219
2018211
2017195

Top Attributes

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Topic's top 5 most impactful authors

Timothy A. Shahan

28 papers, 1K citations

Kennon A. Lattal

27 papers, 929 citations

Edmund Fantino

21 papers, 1.9K citations

Wayne W. Fisher

19 papers, 1.1K citations

Frances K. McSweeney

18 papers, 477 citations