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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

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,...
Operant Conditioning01:21

Operant Conditioning

Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
Reinforcement in operant conditioning can be positive or negative, both of which serve to increase the likelihood of a behavior. Positive...
Reinforcement01:23

Reinforcement

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:
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

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. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant factor...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
Incentive Theory: Pull Theory of Motivation01:18

Incentive Theory: Pull Theory of Motivation

Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
The theory differentiates between intrinsic and...

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Related Experiment Video

Updated: Jul 16, 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

Customer Baseline Credibility in Constrained Reinforcement Learning for Incentive-Based Demand Response.

Jiyong Li1, Kaiyue Wang1

  • 1Department of Electrical Engineering, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a new method using customer baseline load (CBL) credibility to improve incentive allocation in demand response (DR) programs. The approach enhances operational performance and settlement reliability for power systems integrating renewable energy.

Keywords:
action correctionconstrained reinforcement learningcustomer baseline credibilityincentive settlementincentive-based demand responserenewable energy accommodationresource allocation

Related Experiment Videos

Last Updated: Jul 16, 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

Area of Science:

  • Power Systems Engineering
  • Artificial Intelligence
  • Energy Economics

Background:

  • Incentive-based demand response (DR) is crucial for power system flexibility with high renewable energy integration.
  • Accurate allocation of DR incentives is challenged by user response uncertainty and the credibility of customer baseline load (CBL) estimations.
  • CBL credibility directly impacts response measurement, verification, and the fairness of incentive settlements.

Purpose of the Study:

  • To propose a constrained reinforcement learning (CRL) method that incorporates CBL credibility for dynamic resource allocation in incentive-based DR.
  • To evaluate user resources based on flexible capacity, reliability, cost, and CBL credibility for improved DR management.
  • To dynamically determine incentive multipliers and task allocation ratios using a group-level reinforcement learning agent.

Main Methods:

  • Developed a constrained reinforcement learning framework (CBL-CRL) integrating CBL credibility as a pre-event allocation factor.
  • Evaluated user resources using flexible capacity, response reliability, cost, and CBL credibility scores.
  • Employed a group-level reinforcement learning agent for dynamic incentive and task allocation, with an action correction module for constraint adherence.

Main Results:

  • The CBL-CRL method reduced normalized total operating cost by 10.3% compared to a no-DR scenario.
  • Achieved a 10.7% reduction in response tracking error and a 40.8% decrease in CBL risk exposure compared to the MAPPO baseline.
  • Demonstrated a superior balance between operational performance, incentive efficiency, action feasibility, and settlement reliability.

Conclusions:

  • CBL credibility is an effective pre-event resource allocation factor for measurement-driven DR programs, not just for post-event settlement.
  • The proposed CBL-CRL method enhances the reliability and efficiency of incentive-based DR in power systems with significant renewable energy penetration.
  • Integrating CBL credibility into DR resource allocation improves overall system performance and settlement accuracy.