Results 161 to 170 of about 580,002 (195)

Secondary Reinforcement as a Function of Reinforcement Scheduling

Psychological Reports, 1956
In the present study, albino rats were given discrimination training in a modified Skinner Box. During this training several schedules of SD—SR pairings were presented; differential effects of this training were measured during extinction, which also involved a further scheduling of the R—Sr pairings.
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The Psychobiology of Reinforcers

Annual Review of Psychology, 1992
INTRODUCT ION .. . . . . . . .. .. . . . . . . . . .. . . . . .. . . . . .... . . .. . ... . . .... . . . 443 ENHANCING FUNCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 Memory Consolidation . . . . . . . . . . . . . . . . . . . . . . .
Norman M. White, Peter M. Milner
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SECONDARY REINFORCEMENT AS A FUNCTION OF THE NUMBER OF REINFORCED TRIALS1

Child Development, 1961
The purpose of this study was to observe the effects of varying habit strength sustained through secondary and primary reinforcers upon extinction behavior of children in an operant situation. A few studies (I, 3, 4, 6) have investigated the relation between number of training trials and extinction with secondary reinforcement of rats.
Nancy Angrist Myers, Grace J. Craig, Jerome L. Myers   +2 more
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Longitudinal Reinforcement Limits for Fiber-Reinforced Polymer Reinforced Concrete Members

ACI Structural Journal, 2017
Adequate ductility is an important issue for fiber-reinforced polymer (FRP) reinforced concrete (RC) members because of the brittle behavior of FRP bars and concrete. FRP reinforcement in the tension side needs to be sufficient to prevent the brittle rupture of the FRP bars and attain the ultimate compressive strain in concrete in the compression side.
Karim, Hogr, Sheikh, M Neaz, Hadi, Muhammad N. S   +2 more
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Reinforcement Schedule and the Durability of A Secondary Reinforcer

The Psychological Record, 1965
To investigate the possibility of PRE upon an Sr, a neutral stimulus (light) was paired with primary reinforcement (candy) on three different reinforcement schedules, with no response required of S. The light was presented for 18 trials. On reinforced trials, candy appeared simultaneously with the light.
Marilyn E. Miller, Jack Weidner
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Human-level control through deep reinforcement learning

Nature, 2015
Volodymyr Mnih, K. Kavukcuoglu, David Silver, Andrei A. Rusu, J. Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, A. Fidjeland, Georg Ostrovski, Stig Petersen, Charlie Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis   +18 more
semanticscholar   +1 more source

Differential Reinforcement Effectiveness as a Function of Schedule of Reinforcement and Reinforcement History

The Psychological Record, 1975
Variants of the differential reinforcement paradigm were investigated as a function of both present schedule of reinforcement and past reinforcement history. Results suggest that such differential reinforcement procedures are effective in both increasing new responses and decreasing old responses. Reinforcement history was found not to be a significant
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Speciation by reinforcement

Trends in Ecology & Evolution, 1987
Speciation has a central place in evolution, linking genetic processes within populations to the generation of biological diversity. The formation of new species must involve the evolution of barriers to gene flow within existing species, but how these barriers arise remains a problem.
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Grandmaster level in StarCraft II using multi-agent reinforcement learning

Nature, 2019
O. Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David Choi, Richard Powell, T. Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, M. Kroiss, Ivo Danihelka, Aja Huang, L. Sifre, Trevor Cai, J. Agapiou, Max Jaderberg, A. Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, D. Budden, Yury Sulsky, James Molloy, T. Paine, Caglar Gulcehre, Ziyun Wang, T. Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, David Silver   +41 more
semanticscholar   +1 more source

Introduction to Reinforcement Learning

Deep Reinforcement Learning with Python, 2020
Mohit Sewak
semanticscholar   +1 more source