Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

436
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,...
436
Instinctive Drift01:05

Instinctive Drift

607
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
607
Reinforcement01:23

Reinforcement

786
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:
786
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

433
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
433
Observational Learning01:12

Observational Learning

791
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...
791
Law of Effect01:06

Law of Effect

2.4K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
2.4K

You might also read

Related Articles

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

Sort by
Same author

[An analysis of the cause and countermeasure of death of patients with severe obstructive sleep apnea hypopnea syndrome].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2010
Same author

Involvement of ERK 1/2 activation in electroacupuncture pretreatment via cannabinoid CB1 receptor in rats.

Brain research·2010
Same author

The Regional Network for Asian Schistosomiasis and Other Helminth Zoonoses (RNAS(+)) target diseases in face of climate change.

Advances in parasitology·2010
Same author

Monomeric type I and type III transforming growth factor-β receptors and their dimerization revealed by single-molecule imaging.

Cell research·2010
Same author

Quantitative prediction of the thermal motion and intrinsic disorder of protein cofactors in crystalline state: a case study on halide anions.

Journal of theoretical biology·2010
Same author

Structure determination of selaginellins G and H from Selaginella pulvinata by NMR spectroscopy.

Magnetic resonance in chemistry : MRC·2010

Related Experiment Video

Updated: Jan 8, 2026

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.0K

Network traffic control method of NHP based on deep reinforcement learning.

Qinglin Huang1, Zhizhong Tan2, Qiang Wang3

  • 1School of Information Engineering, Huzhou University, Huzhou, 313000, China.

Scientific Reports
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Dueling Double Deep Q-Network (D3QN) for optimizing network traffic control in Network-Infrastructure Hiding Protocol (NHP) environments. The new method enhances Quality of Service (QoS) by improving throughput and reducing latency and packet loss.

Keywords:
D3QN algorithmDeep Reinforcement LearningNetwork traffic controlNetwork-Infrastructure Hiding ProtocolPerformance optimization

More Related Videos

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.5K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.4K

Related Experiment Videos

Last Updated: Jan 8, 2026

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.0K
Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
08:42

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

12.5K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.4K

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Network-Infrastructure Hiding Protocol (NHP) enhances security by concealing resources and limiting access.
  • Optimizing Quality of Service (QoS) in NHP environments presents significant traffic control challenges.
  • Existing Deep Reinforcement Learning (DRL) methods for Software-Defined Networking (SDN) in NHP lack dynamic adaptability and optimization effectiveness.

Purpose of the Study:

  • To propose an intelligent network traffic control method for NHP environments using DRL.
  • To address limitations in current QoS optimization and dynamic adaptability within NHP networks.
  • To enhance performance-security collaboration in next-generation network management.

Main Methods:

  • Development of an intelligent regulation method based on DRL.
  • Implementation of the Dueling Double Deep Q-Network (D3QN) algorithm to create an agent system.
  • Real-time network state perception and autonomous decision-making for traffic control.

Main Results:

  • The proposed D3QN method significantly outperforms traditional algorithms in throughput, latency, and packet loss rate.
  • Demonstrated exceptional adaptability and stability in dynamic network conditions.
  • Achieved superior performance in key Quality of Service (QoS) indicators.

Conclusions:

  • The D3QN-based approach offers an efficient and reliable intelligent control solution for traffic optimization in complex NHP networks.
  • Provides novel theoretical and practical pathways for performance-security collaboration.
  • Shows strong practical value and promising application prospects for future network management systems.