Jove
Visualize
Contact Us

Related Concept Videos

Reinforcement01:23

Reinforcement

310
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:
310
Reinforcement Schedules01:24

Reinforcement Schedules

229
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,...
229
Machines: Problem Solving II01:30

Machines: Problem Solving II

355
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
355
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.2K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.2K
Observational Learning01:12

Observational Learning

269
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...
269
Machines: Problem Solving I01:22

Machines: Problem Solving I

390
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
390

You might also read

Related Articles

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

Sort by
Same author

Cost-Efficient Approaches for Fulfillment of Functional Coverage during Verification of Digital Designs.

Micromachinesยท2022
See all related articles
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 Experiment Video

Updated: Aug 22, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Reinforcement Learning Made Affordable for Hardware Verification Engineers.

Alexandru Dinu1, Petre Lucian Ogrutan1

  • 1Department of Electronics and Computers, Transilvania University of Brasov, 500036 Brasov, Romania.

Micromachines
|November 11, 2022
PubMed
Summary

Reinforcement learning (RL) automates digital design verification by generating input stimuli more efficiently than traditional methods. This AI approach significantly reduces the steps needed to reach desired functional states in complex hardware designs.

Keywords:
automationdigital designepsilon-greedy algorithmfunctional verificationreinforcement learningsoftware systemstimuli generation

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

10.4K
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

Related Experiment Videos

Last Updated: Aug 22, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K
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

10.4K
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

Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Software Development

Background:

  • Increasing complexity of digital designs necessitates advanced simulation and verification environments.
  • Constrained random stimulus generation is becoming insufficient for comprehensive functional simulation.
  • Powerful computational mechanisms and software are required to exploit modern hardware capabilities.

Purpose of the Study:

  • To introduce a novel software system for automating digital design verification.
  • To leverage reinforcement learning (RL) for efficient input stimuli generation and simulation output analysis.
  • To detail the setup of an RL-based method for automated verification process optimization.

Main Methods:

  • Utilized reinforcement learning (RL), an artificial intelligence technique, to exploit computational resources.
  • Developed a novel software system to simplify analysis of simulation outputs.
  • Configured RL algorithms to generate input stimuli tailored to specific digital design characteristics.

Main Results:

  • Achieved significant efficiency gains in reaching target functional states for digital designs.
  • Demonstrated RL's capability to uncover subtle correlations between design parameters, stimuli, and functional states.
  • RL-based stimulus generation required up to 52 times fewer steps compared to constrained random methods.

Conclusions:

  • Reinforcement learning offers a more efficient and automated approach to digital design verification.
  • Proper configuration of RL algorithms is key for verification engineers to adopt this method.
  • This AI-driven stimulus generation accelerates the process of bringing digital designs to desired functional states.