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

Coordination Number and Geometry02:57

Coordination Number and Geometry

15.2K
For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
15.2K
Reinforcement Schedules01:24

Reinforcement Schedules

108
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,...
108
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

9.4K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
9.4K
Reinforcement01:23

Reinforcement

148
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:
148
Associative Learning01:27

Associative Learning

236
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...
236
Observational Learning01:12

Observational Learning

97
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...
97

You might also read

Related Articles

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

Sort by
Same author

DNA Nanotechnology-Enabled Precise Regulation of Nanozymes and Their Applications.

Research (Washington, D.C.)·2026
Same author

Low-Temperature Ethanol Gas Sensor Based on MoO<sub>3</sub>/Nb<sub>2</sub>C MXene Composite via Crystal Engineering and Facet Release.

Sensors (Basel, Switzerland)·2026
Same author

Continuous flow modular synthetic platform for accelerated drug discovery.

European journal of medicinal chemistry·2026
Same author

Deep-learning-empowered programmable topolectrical circuits.

Nature communications·2026
Same author

Effects of Wearable Continuous Normal-Force Tactile Feedback Applied to the Fingertip on Standing Balance in Healthy Young Adults.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Nano-Enabled Fluorescence Switching: A Novel Strategy for PDGFRβ Detection and TKI Therapy Monitoring.

Research (Washington, D.C.)·2026
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: May 9, 2025

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
07:30

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

Published on: March 21, 2019

7.8K

Influence Enhanced Sparse Coordination Graphs for Multi-Agent Reinforcement Learning.

Xiwen Zhang1, Jie Chen1, Ming-Gang Gan1

  • 1State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing, 100081, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Influence Enhanced Sparse Coordination Graphs (IESCG) to improve multi-agent reinforcement learning by better modeling agent collaboration. The new method enhances value function expressiveness, leading to faster convergence and higher win rates in complex scenarios.

Keywords:
Coordination graphsDecentralized partially observable Markov decision process (Dec-POMDP)Multi-agent reinforcement learningQ-learning

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Related Experiment Videos

Last Updated: May 9, 2025

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
07:30

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

Published on: March 21, 2019

7.8K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Multi-Agent Systems

Background:

  • Value decomposition methods in Multi-Agent Reinforcement Learning (MARL) often neglect inter-agent collaboration, limiting performance.
  • Existing coordination graph approaches use simplistic rules, failing to capture complex collaborative relationships.

Purpose of the Study:

  • To propose Influence Enhanced Sparse Coordination Graphs (IESCG) for improved value function expressiveness in MARL.
  • To address limitations in modeling inter-agent collaboration within complex environments.

Main Methods:

  • Influence networks are proposed to quantitatively describe agent collaboration importance.
  • Sparse Time-Varying Coordination Graphs are constructed using influence networks.
  • Recurrent Payoff Function Networks (RPFN) incorporate temporal information for influence networks.
  • Sparse Graph Advantage Selection Coefficients (SGASC) stabilize value functions for training.

Main Results:

  • The proposed algorithm accelerates convergence in MARL tasks.
  • Improved winning probabilities were observed in experimental benchmarks.
  • The method shows significant advantages in complex multi-agent scenarios.

Conclusions:

  • IESCG effectively models inter-agent collaboration, enhancing MARL performance.
  • The integration of influence networks and temporal information leads to more robust agent coordination.
  • The approach offers a promising direction for advancing MARL research in complex settings.