Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reinforcement

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:

Reinforcement Schedules

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,...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adrenaline induces oxidative stress-mediated perturbation in cardiac riboflavin metabolism through direct interaction with flavin coenzymes: protection by melatonin-a mechanistic insight.

The Journal of nutritional biochemistry·2026

Same author

Genomic and phenotypic insights into the probiotic and antimicrobial potential of Bacillus amyloliquefaciens BCSIR-AJ strain from garlic chutney.

Archives of microbiology·2026

Same author

Geochemistry and radiation risk of radium isotopes and heavy metal contamination in groundwater of Dhaka City, Bangladesh.

Environmental science. Processes & impacts·2026

Same author

Genomic and phenotypic characterization of non-O157 Shiga toxin-producing <i>Escherichia coli</i> from Bangladeshi cattle reveals diverse virulence and resistance profiles.

Microbiology spectrum·2026

Same author

Draft genome sequence of an alkaliphilic drug-resistant <i>Burkholderia cenocepacia_DG4</i> strain isolated from commercial liquid detergent in Bangladesh.

Microbiology resource announcements·2026

Same author

Advancing biomedical data analytics using explainable neural network-based learning model for progressive neurodegenerative disorder diagnosis.

Scientific reports·2026

Search research articles

Related Experiment Videos

A dynamic reward framework for scalable and efficient IoT-WSN routing using deep reinforcement learning.

Suresh Betam¹, Sanam Nagendram², Bathula Prasanna Kumar³

¹Department of CSE, KL Deemed to be University, Vaddeswaram, Andhra Pradesh, India.

Scientific Reports

|July 11, 2026

Summary

This study introduces dynamic reward structuring for deep reinforcement learning in Internet of Things-based wireless sensor networks (IoT-WSNs). The novel framework optimizes energy efficiency, reduces latency, and boosts throughput for adaptive routing.

Keywords:

Deep reinforcement learning Dynamic reward structuring IoT-WSN Multi-agent systems Multi-objective optimization Routing optimization

Related Experiment Videos

Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Internet of Things-based wireless sensor networks (IoT-WSNs) face challenges in energy consumption, latency, and congestion.
Conventional reinforcement learning methods struggle with dynamic network topologies and multi-objective optimization due to static reward functions.

Purpose of the Study:

To propose a dynamic reward structuring framework using deep reinforcement learning for adaptive and balanced routing in IoT-WSNs.
To enable joint optimization of energy efficiency, delay, and throughput under dynamic network conditions.

Main Methods:

Developed a dynamic reward structuring framework with real-time reward recalibration.
Integrated value-based, policy-based, and actor-critic methods into a hybrid deep reinforcement learning architecture.
Employed multi-agent coordination, attention mechanisms, and a hierarchical learning structure for scalability and efficiency.

Main Results:

Achieved approximately 30% improvement in energy efficiency.
Demonstrated a 25% reduction in network latency.
Showcased a 35% increase in network throughput compared to baseline methods.

Conclusions:

Dynamic reward adaptation is effective for scalable and robust multi-objective optimization in IoT-WSN routing.
The proposed framework significantly enhances IoT-WSN performance metrics.