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

410
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:
410
Observational Learning01:12

Observational Learning

360
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...
360
Associative Learning01:27

Associative Learning

637
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...
637
Reinforcement Schedules01:24

Reinforcement Schedules

257
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,...
257
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
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

147
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...
147

You might also read

Related Articles

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

Sort by
Same author

Searching to Modulate for Cold-Start Recommendation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Research on the Impact of Shot Selection on Neuromuscular Control Strategies During Basketball Shooting.

Sensors (Basel, Switzerland)·2025
Same author

Cognition-Oriented Multiagent Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2025
Same author

A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2024
Same author

Demosaicking DoFP images using edge compensation method based on correlation.

Optics express·2023
Same author

Multiexperience-Assisted Efficient Multiagent Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2023
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: Oct 3, 2025

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.7K

Attention Enhanced Reinforcement Learning for Multi agent Cooperation.

Zhiqiang Pu, Huimu Wang, Zhen Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |February 18, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Attention Enhanced Reinforcement Learning (AERL) improves multi-agent cooperation by addressing complex interactions and communication limits. This novel method enhances coordination in dynamic environments, demonstrating robust performance in simulations.

    More Related Videos

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.8K
    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
    09:13

    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

    Published on: May 3, 2012

    14.5K

    Related Experiment Videos

    Last Updated: Oct 3, 2025

    Automated Interactive Video Playback for Studies of Animal Communication
    07:21

    Automated Interactive Video Playback for Studies of Animal Communication

    Published on: February 9, 2011

    13.7K
    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    7.8K
    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
    09:13

    A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

    Published on: May 3, 2012

    14.5K

    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Control Systems

    Background:

    • Multi-agent systems face challenges in complex interactions and limited communication.
    • Dynamic communication topologies and range limitations hinder cooperative task performance.
    • Existing reinforcement learning methods struggle with scalability and convergence in multi-agent settings.

    Purpose of the Study:

    • To propose a novel Attention Enhanced Reinforcement Learning (AERL) method for effective multi-agent cooperation.
    • To overcome limitations in communication range, complex interactions, and time-varying communication topologies.
    • To enhance scalability and convergence speed in training large-scale multi-agent systems.

    Main Methods:

    • Developed a Communication Enhanced Network (CEN) utilizing graph attention mechanisms to expand communication range and manage intricate agent interactions.
    • Integrated a Graph Spatiotemporal Long Short-Term Memory (GST-LSTM) network to preserve spatial structures while capturing temporal dependencies.
    • Implemented a Parameter Sharing Multi-Pseudo Critic Proximal Policy Optimization (PS-MPC-PPO) for scalability and bias mitigation in training.

    Main Results:

    • Simulation results validated the effectiveness of AERL across formation control, group containment, and predator-prey scenarios.
    • The proposed method demonstrated robust performance in addressing complex interactions and communication constraints.
    • AERL showed improved convergence and scalability compared to standard methods in large-scale agent training.

    Conclusions:

    • AERL offers a significant advancement in multi-agent cooperation, effectively handling complex interactions and communication limitations.
    • The integration of graph attention and advanced reinforcement learning techniques provides a robust framework for cooperative tasks.
    • The method's effectiveness and robustness are confirmed through diverse simulation environments, highlighting its practical applicability.