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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

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In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
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Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
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Related Experiment Video

Updated: Feb 28, 2026

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

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EPRS: Experience-Prioritized Reinforcement Scheduler in Edge Clusters.

Shuya Tan1, Tiancong Huang1, Enguo Zhu2

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

A new container-based scheduling framework with an Experience-Prioritized Reinforcement Scheduler (EPRS) enhances load balancing in edge computing. This approach improves resource utilization and performance in dynamic edge environments.

Keywords:
edge clusteringload balancingreinforcement learningtask scheduling

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Last Updated: Feb 28, 2026

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09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

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Area of Science:

  • Distributed Systems
  • Artificial Intelligence
  • Computer Science

Background:

  • Edge computing environments are dynamic and heterogeneous, posing challenges for task scheduling.
  • Conventional scheduling algorithms struggle with load fluctuations, leading to imbalance and underutilization.
  • Effective load balancing is crucial for optimizing resource utilization in edge clusters.

Purpose of the Study:

  • To propose a container-based edge cluster scheduling framework for enhanced load balancing.
  • To introduce an Experience-Prioritized Reinforcement Scheduler (EPRS) for adaptive task scheduling.
  • To improve multi-dimensional resource allocation in heterogeneous edge environments.

Main Methods:

  • Developed a container-based scheduling framework with a real-time resource monitor.
  • Implemented an Experience-Prioritized Reinforcement Scheduler (EPRS) using priority-driven sample selection.
  • Optimized resource allocation by considering node metrics and task requirements in a Kubernetes-based cluster.

Main Results:

  • The proposed framework significantly improved multi-dimensional load balancing performance.
  • Achieved an average gain of 28.25% over existing reinforcement learning-based schedulers.
  • Demonstrated a 29.78% improvement compared to traditional scheduling algorithms.

Conclusions:

  • The EPRS-integrated framework effectively addresses load balancing challenges in edge computing.
  • The approach enhances resource utilization and scheduling efficiency in dynamic edge environments.
  • Validated through implementation and experiments on a Kubernetes-based edge cluster.