نتایج جستجو برای: differential reinforcement

تعداد نتایج: 324469  

Journal: :The Journal of pharmacology and experimental therapeutics 1984
J M O'Donnell L S Seiden

Performance maintained by differential-reinforcement-of-low-rate operant schedules has been found to be sensitive to antidepressant drugs. Tricyclic antidepressants, monoamine oxidase inhibitors and atypical antidepressants reduce response rate and increase reinforcement rate under long differential-reinforcement-of-low-rate schedules. In order to study the neurochemical mechanism by which the ...

Journal: :Journal of the experimental analysis of behavior 1988
A A Imam K A Lattal

The effects of two alternative sources of food delivery on the key-peck responding of pigeons were examined. Pecking was maintained by a variable-interval 3-min schedule. In the presence of this schedule in different conditions, either a variable-time 3-min schedule delivering food independently of responding or an equivalent schedule that required a minimum 2-s pause between a key peck and foo...

Journal: :Journal of the experimental analysis of behavior 2011
Allen Karsina Rachel H Thompson Nicole M Rodriguez

The effects of a history of differential reinforcement for selecting a free-choice versus a restricted-choice stimulus arrangement on the subsequent responding of 7 undergraduates in a computer-based game of chance were examined using a concurrent-chains arrangement and a multiple-baseline-across-participants design. In the free-choice arrangement, participants selected three numbers, in any or...

2016
Borja Balle Maziar Gomrokchi Doina Precup

We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples.

Journal: :CoRR 2018
Andrew Cohen Lei Yu Robert Wright

We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theory explaining why diversity in behavior policies enables effective exploration ...

Journal: :Journal of the Experimental Analysis of Behavior 1963

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