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

Reinforcement01:23

Reinforcement

304
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:
304
Observational Learning01:12

Observational Learning

250
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...
250
Reinforcement Schedules01:24

Reinforcement Schedules

223
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,...
223
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

134
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
134
Associative Learning01:27

Associative Learning

474
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...
474
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
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...
1.8K

You might also read

Related Articles

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

Sort by
Same author

RBM10 Deficiency Promotes Anti-PD-1 Resistance in LUAD via STING Alternative Splicing-Driven CCL7 Signaling and Macrophage Polarization.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Vertical Interaction between Thiourea and Perovskite Surface Results in Obviously Enhanced Performance with PCE Surpassing 24% Efficiency.

ACS applied materials & interfaces·2026
Same author

The mutated CYTOKININ OXIDASE/DEHYDROGENASE 7 promotes cell division in pith and plays a critical role in the development of stem lettuce.

The Plant journal : for cell and molecular biology·2026
Same author

Analysis of the effect and correlation of the co-care model on the diagnosis and treatment of type 2 diabetes patients.

Open medicine (Warsaw, Poland)·2026
Same author

Effect of a 5:2 intermittent fasting diet on obese patients with polycystic ovary syndrome.

Frontiers in endocrinology·2026
Same author

Experimental study on the effect of capillary inner diameter on 69.8 nm laser generation and analysis of the Z-pinch plasma state.

Optics express·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Aug 3, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

37

Multiagent Reinforcement Learning With Graphical Mutual Information Maximization.

Shifei Ding, Wei Du, Ling Ding

    IEEE Transactions on Neural Networks and Learning Systems
    |April 7, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new graph neural network (GNN) method for multiagent reinforcement learning (MARL) that enhances agent communication by maximizing mutual information. The approach improves cooperative task performance by better utilizing agent features and topological relationships.

    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

    9.4K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.8K

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    37
    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

    9.4K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.8K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Communication learning is crucial in multiagent reinforcement learning (MARL).
    • Graph neural networks (GNNs) aggregate neighbor information for representation learning in MARL.
    • Existing GNN-based MARL methods may not fully extract useful information or consider topological relationships.

    Purpose of the Study:

    • To develop a novel GNN-based MARL method for efficient information extraction from neighbor agents.
    • To obtain high-quality feature representations for enhanced cooperation in multiagent systems.
    • To maximize the correlation between agent features and hidden representations using graphical mutual information.

    Main Methods:

    • Proposed a novel GNN-based MARL method incorporating graphical mutual information (MI) maximization.
    • Extended MI optimization to multiagent systems, considering both agent features and topological relationships.
    • The method is agnostic to specific MARL algorithms and integrates with value function decomposition methods.

    Main Results:

    • The proposed method demonstrated superior performance compared to existing MARL approaches on various benchmarks.
    • Effective extraction and utilization of neighbor agent information within the graph structure were achieved.
    • High-quality expressive feature representations were obtained, leading to improved cooperative task completion.

    Conclusions:

    • The novel GNN-based MARL method with graphical mutual information maximization significantly enhances agent communication and cooperation.
    • The approach effectively leverages agent features and topological information for better coordination.
    • This method offers a flexible and powerful extension for various MARL frameworks.