• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Wednesday, October 1, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News

Discrete-time rewards efficiently guide the extraction of continuous-time optimal control policy from system data

by
June 28, 2024
in Science News
Reading Time: 3 mins read
0
Schematic framework of the reinforcement learning algorithm using policy iteration for continuous-time dynamical systems
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

This study is led by an international team of scientists including Dr. Ci Chen (School of Automation, Guangdong University of Technology, China), Dr. Lihua Xie (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore), and Dr. Shengli Xie (Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangdong Key Laboratory of IoT Information Technology, China), co-contributed by Dr. Yilu Liu (Department of Electrical Engineering and Computer Science, University of Tennessee, USA) and Dr. Frank L. Lewis (UTA Research Institute, The University of Texas at Arlington, USA).

Schematic framework of the reinforcement learning algorithm using policy iteration for continuous-time dynamical systems

Credit: ©Science China Press

This study is led by an international team of scientists including Dr. Ci Chen (School of Automation, Guangdong University of Technology, China), Dr. Lihua Xie (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore), and Dr. Shengli Xie (Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangdong Key Laboratory of IoT Information Technology, China), co-contributed by Dr. Yilu Liu (Department of Electrical Engineering and Computer Science, University of Tennessee, USA) and Dr. Frank L. Lewis (UTA Research Institute, The University of Texas at Arlington, USA).

The concept of Reward is central in reinforcement learning and is also widely used in the natural sciences, engineering, and social sciences. Organisms learn behavior by interacting with their environment and observing the resulting rewarding stimuli. The expression of rewards largely represents the perception of the system and defines the behavioral state of the dynamic system. In reinforcement learning, finding rewards that explain behavioral decisions of dynamic systems has been an open challenge.

The work aims to propose reinforcement learning algorithms using discrete-time rewards in both continuous time and action space, where the continuous space corresponds to the phenomena or behaviors of a system described by the laws of physics. The approach of feeding state derivatives back into the learning process has led to the development of an analytical framework for reinforcement learning based on discrete-time rewards, which is essentially different from existing integral reinforcement learning frameworks. “When the idea of feedbacking the derivative into the learning process struck, it felt like lightning! And guess what? It mathematically ties into the discrete-time reward-based policy learning!” Chen recalls his Eureka moment and says.

Under the guidance of discrete-time reward, the search process of behavioral decision law is divided into two stages: feed-forward signal learning and feedback gain learning. In their study, it was found that the optimal decision law for continuous-time dynamic systems can be searched from real-time data of dynamic systems using the discrete-time reward-based technique. The above method has been applied to power system state regulation to achieve optimal design of output feedback. This process eliminates the intermediate stage of identifying dynamic models and significantly improves the computational efficiency by removing the reward integrator operator from the existing integral reinforcement learning framework.

This research uses discrete-time reward guidance to discover optimization strategies for continuous-time dynamical systems, and constructs a computational tool for understanding and improving dynamical systems. This result can play an important role in natural science, engineering, and social science.

This work was supported by the National Natural Science Foundation of China and the Fundamental and Applied Basic Research Fund of Guangdong Province.

 

See the article:

Learning the Continuous-Time Optimal Decision Law from Discrete-Time Rewards

https://doi.org/10.1360/nso/20230054



Journal

National Science Open

DOI

10.1360/nso/20230054

Share12Tweet8Share2ShareShareShare2

Related Posts

Endocervical Curettage Detects CIN2+ in Postmenopausal Women

October 1, 2025
Can AI Influence You to Adopt Veganism—or Engage in Self-Harm?

Can AI Influence You to Adopt Veganism—or Engage in Self-Harm?

October 1, 2025

Revolutionary Algorithm Enhances Disease Classification Using Omics

October 1, 2025

Advancements in Low-Dimensional Materials for Bioelectronics

October 1, 2025

POPULAR NEWS

  • New Study Reveals the Science Behind Exercise and Weight Loss

    New Study Reveals the Science Behind Exercise and Weight Loss

    90 shares
    Share 36 Tweet 23
  • Physicists Develop Visible Time Crystal for the First Time

    74 shares
    Share 30 Tweet 19
  • How Donor Human Milk Storage Impacts Gut Health in Preemies

    63 shares
    Share 25 Tweet 16
  • New Study Indicates Children’s Risk of Long COVID Could Double Following a Second Infection – The Lancet Infectious Diseases

    63 shares
    Share 25 Tweet 16

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Endocervical Curettage Detects CIN2+ in Postmenopausal Women

Can AI Influence You to Adopt Veganism—or Engage in Self-Harm?

Revolutionary Algorithm Enhances Disease Classification Using Omics

Subscribe to Blog via Email

Enter your email address to subscribe to this blog and receive notifications of new posts by email.

Join 60 other subscribers
  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.