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

Observational Learning01:12

Observational Learning

285
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...
285
Reinforcement01:23

Reinforcement

313
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:
313
Reinforcement Schedules01:24

Reinforcement Schedules

231
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,...
231
Associative Learning01:27

Associative Learning

533
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
533
Cognitive Learning01:21

Cognitive Learning

484
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
484
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.9K

You might also read

Related Articles

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

Sort by
Same author

A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates.

Sensors (Basel, Switzerland)·2021
Same author

EERS: Energy-Efficient Reference Node Selection Algorithm for Synchronization in Industrial Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2020
See all related articles

Related Experiment Video

Updated: Aug 27, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach.

Hongrok Choi1, Sangheon Pack1

  • 1School of Electrical Engineering, Korea University, Seoul 02841, Korea.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary

This study introduces a deep reinforcement learning (DRL) approach for cooperative satellite downloading, enhancing data transmission efficiency in low Earth orbit (LEO) by maximizing contact time utilization.

Keywords:
deep reinforcement learning (DRL)graph attention network (GAT)low earth orbit (LEO) satellitesoft actor-critic (SAC)

More Related Videos

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.2K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.0K

Related Experiment Videos

Last Updated: Aug 27, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.2K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

5.0K

Area of Science:

  • Computer Science
  • Aerospace Engineering
  • Telecommunications

Background:

  • Low Earth Orbit (LEO) satellites face challenges in data transmission to ground stations due to high mobility and limited contact time.
  • Efficient data offloading is critical for LEO satellite applications like remote sensing and surveillance.

Purpose of the Study:

  • To develop a cooperative downloading scheme for LEO satellites to maximize data transmission during limited contact windows.
  • To leverage inter-satellite communication links (ISLs) for improved data downloading capabilities.

Main Methods:

  • Formulation of a Markov decision problem (MDP) to optimize data download volume.
  • Application of a soft-actor-critic (SAC)-based deep reinforcement learning (DRL) algorithm.
  • Design of a novel neural network integrating graph attention (GAT) and fully connected (FC) layers for network feature extraction and satellite control.

Main Results:

  • The proposed DRL-based cooperative downloading scheme significantly improves data offloading efficiency.
  • Achieved up to 17.8% increase in average contact time utilization compared to independent or random offloading methods.

Conclusions:

  • Deep reinforcement learning offers an effective solution for optimizing data downloads in mobile LEO satellite networks.
  • The developed cooperative downloading strategy enhances the overall utility of satellite communication systems.