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 Experiment Videos

A parameter control method in reinforcement learning to rapidly follow unexpected environmental changes.

Kazushi Murakoshi1, Junya Mizuno

  • 1Department of Knowledge-based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi 441-8580, Japan. mura@tutkie.tut.ac.jp

Bio Systems
|November 6, 2004
PubMed
Summary

This study introduces a novel parameter control method for reinforcement learning agents, enabling rapid adaptation to unexpected environmental changes. The approach ensures agents can quickly respond to emergencies, regardless of the learning algorithm or problem type.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Image correction method for the colour contrast effect using inverse processes of the brain.

Bio Systems·2010
Same author

A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing.

Bio Systems·2008
Same author

A neural circuit model for changing the amount of information maintained in short-term memory depending on stimuli relationships.

Bio Systems·2008
Same author

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses.

Bio Systems·2007
Same author

Reducing topological defects in self-organizing maps using multiple scale neighborhood functions.

Bio Systems·2006
Same author

Avoiding overfitting in multilayer perceptrons with feeling-of-knowing using self-organizing maps.

Bio Systems·2005

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Reinforcement learning (RL) agents often struggle with rapid adaptation to dynamic environments.
  • Existing RL algorithms may exhibit slow responses to unexpected environmental shifts.

Purpose of the Study:

  • To develop a parameter control method for RL that enhances adaptability to sudden environmental changes.
  • To investigate the influence of neuromodulators on adaptive behaviors in emergency scenarios.

Main Methods:

  • Proposing a parameter control method that adjusts learning parameters based on behavior-neuromodulator relationships.
  • Utilizing an 'emergency' keyword to guide the determination of appropriate parameter adjustment directions.
  • Conducting computer experiments with Q-learning and actor-critic (AC) architectures on discontinuous and continuous state-action problems.

Related Experiment Videos

Main Results:

  • Agents employing the proposed method demonstrated significantly faster responses to unexpected environmental changes.
  • The method's effectiveness was consistent across different RL algorithms (Q-learning, AC).
  • The approach proved robust for both discontinuous and continuous state-action problems.

Conclusions:

  • The developed parameter control method enables rapid and adaptive responses in RL agents facing unexpected environmental changes.
  • This approach offers a generalizable solution for enhancing RL agent resilience in dynamic and critical situations.