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

362
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,...
362
Reinforcement01:23

Reinforcement

681
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:
681
Observational Learning01:12

Observational Learning

701
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...
701
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

303
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 of...
303
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.6K

You might also read

Related Articles

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

Sort by
Same author

Duhuo Jisheng Decoction Mitigates Intervertebral Disc Degeneration via Metabolic Reprogramming and TGF-β/Smad-Driven Autophagy-Fibrosis Network Modulation.

Biomedical chromatography : BMC·2026
Same author

Combined inhibition of BETs and HDACs as a potential epigenetics-based therapy for malignant rhabdoid tumor.

Cell death & disease·2026
Same author

Resveratrol alleviates intervertebral disc degeneration by regulating ferroptosis of nucleus pulposus cells.

NPJ systems biology and applications·2026
Same author

Predictive modelling of duodenal stump leakage after gastric cancer and long-term oncological outcomes.

Translational cancer research·2026
Same author

Unveiling LAIR1: a prognostic biomarker associated with gastric cancer progression and metastasis.

Translational cancer research·2026
Same author

Draft genome sequence of <i>Enterobacter ludwigii</i> strain Cas398 isolated from rhizosphere soil of tobacco in China.

Microbiology resource announcements·2026

Related Experiment Video

Updated: Dec 10, 2025

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

9.0K

Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning.

Xiaoqiang Wang, Liangjun Ke, Zhimin Qiao

    IEEE Transactions on Cybernetics
    |September 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Cooperative Double Q-Learning (Co-DQL) enhances traffic signal control (TSC) by improving multiagent reinforcement learning (MARL) scalability and agent cooperation. This new method optimizes traffic flow and outperforms existing algorithms.

    More Related Videos

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.7K

    Related Experiment Videos

    Last Updated: Dec 10, 2025

    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

    9.0K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

    Published on: March 2, 2015

    10.7K

    Area of Science:

    • Artificial Intelligence
    • Transportation Engineering
    • Computer Science

    Background:

    • Traffic signal control (TSC) is complex, especially at large scales.
    • Multiagent reinforcement learning (MARL) shows promise but faces scalability and agent interaction challenges.

    Purpose of the Study:

    • Propose Cooperative Double Q-Learning (Co-DQL), a novel MARL algorithm for large-scale TSC.
    • Address over-estimation and exploration issues in traditional MARL.
    • Enhance agent cooperation and learning stability.

    Main Methods:

    • Utilize a scalable independent double Q-learning with double estimators and Upper Confidence Bound (UCB) policy.
    • Employ mean-field approximation to model agent interactions.
    • Introduce a reward allocation mechanism and local state sharing for stability.

    Main Results:

    • Co-DQL demonstrates improved scalability and cooperative strategy learning.
    • The algorithm effectively mitigates over-estimation while ensuring exploration.
    • Tested on traffic simulators, Co-DQL shows superior performance across various traffic scenarios.

    Conclusions:

    • Co-DQL offers a robust and scalable solution for large-scale traffic signal control.
    • The proposed methods enhance learning stability and agent cooperation in MARL.
    • Co-DQL surpasses current state-of-the-art decentralized MARL algorithms in traffic metrics.