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

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 because...
Avoidance Learning and Learned Helplessness01:14

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...

You might also read

Related Articles

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

Sort by
Same author

RHCR: a reinforced heterogeneous knowledge graph for course recommendation.

Scientific reports·2026
Same author

ATAC-Seq and RNA-Seq Integration Reveals Chromatin Accessibility and Transcriptional Dynamics During Fruit Color Development in Mulberry.

International journal of molecular sciences·2026
Same author

A Fibrous Dressing Integrating Advanced Nanomicro Hybrid Structure with Effective Drug Delivery for Accelerated Wound Healing.

ACS applied bio materials·2025
Same author

Neutrophil-Related Gene Signatures for Ischemic Stroke Diagnosis.

Current medicinal chemistry·2025
Same author

Genome-wide identification and expression analysis of Dof gene family members in mulberry trees (Morus notabilis L.) under drought stress.

BMC genomics·2025
Same author

Research trends of neoadjuvant therapy in lung cancer: a bibliometric analysis.

Discover oncology·2025

Related Experiment Video

Updated: May 21, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

Goal-guided greedy experience replay-enhanced reinforcement learning for efficient autonomous navigation.

Yichun Zeng1, Mingshan Xie2

  • 1College of Big Data and Information Engineering, Guizhou University, Guiyang, China.

Scientific Reports
|May 19, 2026
PubMed
Summary

This study introduces a Goal-guided Greedy Experience Replay Enhanced Reinforcement Learning (GER-RL) method to improve autonomous navigation. By prioritizing valuable experiences, GER-RL enhances data utilization and significantly boosts navigation performance.

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

Related Experiment Videos

Last Updated: May 21, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

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

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep reinforcement learning (DRL) shows promise in mapless goal-driven navigation.
  • Current DRL methods suffer from inefficient experience utilization due to uniform sampling.
  • This underutilization hinders the learning process and overall navigation performance.

Purpose of the Study:

  • To propose an enhanced reinforcement learning method for efficient autonomous navigation.
  • To address the issue of insufficient experience utilization in DRL-based mapless navigation.
  • To improve data efficiency and navigation performance in autonomous agents.

Main Methods:

  • Introduced Goal-guided Greedy Experience Replay Enhanced Reinforcement Learning (GER-RL).
  • Implemented non-uniform experience sampling based on experience importance.
  • Integrated the prioritized experience sampling into a DRL-based navigation model.

Main Results:

  • The GER-RL method effectively prioritizes more beneficial experiences for agent learning.
  • Demonstrated significant improvements in data utilization efficiency during the DRL learning process.
  • Achieved enhanced navigation performance metrics, including higher success rates and reduced collision rates.

Conclusions:

  • The proposed GER-RL method offers a more efficient approach to autonomous navigation.
  • Prioritizing experiences significantly enhances the learning efficiency of DRL agents.
  • GER-RL provides a substantial advancement over existing DRL methods for mapless goal-driven navigation.