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

Observational Learning01:12

Observational Learning

1.2K
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...
1.2K
Reinforcement01:23

Reinforcement

1.1K
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:
1.1K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

3.4K
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...
3.4K
Introduction to Learning01:18

Introduction to Learning

1.4K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.4K
Reinforcement Schedules01:24

Reinforcement Schedules

681
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,...
681
Associative Learning01:27

Associative Learning

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

AI evolution: bring biomimicry to language models.

Bioinspiration & biomimetics·2025
Same author

Mastering the game of Stratego with model-free multiagent reinforcement learning.

Science (New York, N.Y.)·2022
Same author

Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping.

Ultrasonics·2022
Same author

Stable and Efficient Policy Evaluation.

IEEE transactions on neural networks and learning systems·2018
Same author

Optimism in Active Learning.

Computational intelligence and neuroscience·2015
Same author

Algorithmic survey of parametric value function approximation.

IEEE transactions on neural networks and learning systems·2014
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: Mar 21, 2026

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

9.3K

Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning.

Bilal Piot, Matthieu Geist, Olivier Pietquin

    IEEE Transactions on Neural Networks and Learning Systems
    |May 11, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study reveals a formal link between imitation learning (IL) and inverse reinforcement learning (IRL), demonstrating their equivalence through a new set-policy framework. This unifying approach yields novel algorithms that outperform existing methods in learning from expert demonstrations.

    More Related Videos

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    11.2K
    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
    10:39

    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

    Published on: May 3, 2018

    9.2K

    Related Experiment Videos

    Last Updated: Mar 21, 2026

    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

    9.3K
    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
    11:18

    Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

    Published on: June 1, 2015

    11.2K
    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
    10:39

    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

    Published on: May 3, 2018

    9.2K

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Learning from demonstrations is crucial for training agents in dynamic environments.
    • Imitation Learning (IL) and Inverse Reinforcement Learning (IRL) are common but distinct paradigms.
    • Existing IL and IRL methods often have limitations in generalization and robustness.

    Purpose of the Study:

    • To establish a formal, unifying link between Imitation Learning (IL) and Inverse Reinforcement Learning (IRL).
    • To derive novel algorithms by bridging the gap between IL and IRL.
    • To demonstrate the superiority of the proposed unified framework over existing methods.

    Main Methods:

    • Introduced the set-policy framework to connect IL and IRL.
    • Defined an explicit bijective operator (inverse optimal Bellman operator) between IL and IRL solution spaces.
    • Developed new algorithms based on the set-policy framework and compared them to trajectory-matching IRL algorithms.

    Main Results:

    • Demonstrated the formal equivalence between IL and IRL.
    • The set-policy framework provides a unifying approach, enabling IL to handle environment dynamics.
    • New IRL algorithms derived from the set-policy framework significantly outperform standard IL, IRL, and trajectory-matching methods.
    • Achieved more robust solutions compared to existing approaches.

    Conclusions:

    • IL and IRL are not opposite but can be unified, leading to enhanced learning capabilities.
    • The set-policy framework offers a powerful, unified approach for learning from demonstrations.
    • The derived algorithms provide state-of-the-art performance and robustness in agent training.