The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2023.0140838
PDF

Advances in Value-based, Policy-based, and Deep Learning-based Reinforcement Learning

Author 1: Haewon Byeon

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Machine learning is a branch of artificial intelligence in which computers use data to teach themselves and improve their problem-solving abilities. In this case, learning is the process by which computers use data and algorithms to build models that improve performance, and it can be divided into supervised learning, unsupervised learning, and reinforcement learning. Among them, reinforcement learning is a learning method in which AI interacts with the environment and finds the optimal strategy through actions, and it means that AI takes certain actions and learns based on the feedback it receives from the environment. In other words, reinforcement learning is a learning algorithm that allows AI to learn by itself and determine the optimal action for the situation by learning to find patterns hidden in a large amount of data collected through trial and error. In this study, we introduce the main reinforcement learning algorithms: value-based algorithms, policy gradient-based reinforcement learning, reinforcement learning with intrinsic rewards, and deep learning-based reinforcement learning. Reinforcement learning is a technology that enables AI to develop its own problem-solving capabilities, and it has recently gained attention among AI learning methods as the usefulness of the algorithms in various industries has become more widely known. In recent years, reinforcement learning has made rapid progress and achieved remarkable results in a variety of fields. Based on these achievements, reinforcement learning has the potential to positively transform human lives. In the future, more advanced forms of reinforcement learning with enhanced interaction with the environment need to be developed.

Keywords: Reinforcement learning; value-based algorithms; policy gradient-based reinforcement learning; reinforcement learning with intrinsic rewards; deep learning-based reinforcement learning

Haewon Byeon, “Advances in Value-based, Policy-based, and Deep Learning-based Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140838

@article{Byeon2023,
title = {Advances in Value-based, Policy-based, and Deep Learning-based Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140838},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140838},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Haewon Byeon}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org