Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reinforcement

Reinforcement

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:

Reinforcement Schedules

Reinforcement Schedules

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,...

Observational Learning

Observational Learning

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...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

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...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

You might also read

Related Articles

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

Sort by

Same author

Interpretable agentic AI system with localized reasoning for radiology.

NPJ digital medicine·2026

Same author

Global Research Trends of Diabetes Mellitus and Metabolic Reprogramming: A Bibliometric and Visualization Analysis.

Journal of visualized experiments : JoVE·2026

Same author

Exploring the Psychosocial Impact and Developing a Quality of Life Scale for Postpalatoplasty and Noncleft Patients With Functional Articulation Disorders.

The Journal of craniofacial surgery·2026

Same author

CircSSRP1 regulates SMAD3 to affect proliferation and apoptosis of human embryonic palatal mesenchymal cells in nonsyndromic cleft lip with or without cleft palate by sponging miR-708-5p.

Archives of oral biology·2026

Same author

Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.

Journal of biophotonics·2026

Same author

Generative AI-driven enzyme engineering unlocks sustainable valorization of broccoli waste into sulforaphane.

Bioresource technology·2026

Search research articles

Related Experiment Videos

A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning.

Siyuan Guo, Yanchao Sun, Jifeng Hu

IEEE Transactions on Neural Networks and Learning Systems

|November 25, 2025

Summary

This study introduces a Simple Unified Uncertainty-guided (SUNG) framework to improve offline-to-online reinforcement learning (RL). SUNG addresses exploration and distribution shift challenges, enhancing agent performance before deployment.

Related Experiment Videos

Area of Science:

Artificial Intelligence
Machine Learning
Robotics

Background:

Offline reinforcement learning (RL) enables data-driven agent training but often yields suboptimal performance due to limited dataset quality.
Fine-tuning agents with online interactions is crucial for deployment, yet faces challenges like constrained exploration and distribution shift.

Purpose of the Study:

To propose a Simple Unified Uncertainty-guided (SUNG) framework to effectively bridge offline and online reinforcement learning stages.
To address the key challenges of constrained exploratory behavior and state-action distribution shift in offline-to-online RL.

Main Methods:

SUNG quantifies uncertainty using a variational autoencoder (VAE)-based state-action visitation density estimator.
An optimistic exploration strategy selects actions with high value and uncertainty.
An adaptive exploitation method balances conservative offline RL objectives with standard online RL objectives based on uncertainty.

Main Results:

SUNG demonstrates state-of-the-art online finetuning performance across diverse environments and datasets within the D4RL benchmark.
The framework successfully integrates with various existing offline RL methods.
The proposed uncertainty quantification and guided exploration/exploitation strategies effectively mitigate offline-to-online transfer challenges.

Conclusions:

The SUNG framework offers a unified and effective solution for enhancing offline RL agents through online fine-tuning.
Uncertainty estimation is a powerful tool for guiding exploration and managing distribution shift in RL.
SUNG provides a practical approach to improve agent performance and reliability before real-world deployment.