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.2022.0130734
PDF

Multi-Task Reinforcement Meta-Learning in Neural Networks

Author 1: Ghazi Shakah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

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

Abstract: Artificial Neural Networks (ANN) is one of the main and widespread tools for creating intelligent systems. And, they are actively used for data analysis in many areas such as robotics, computer vision, natural language processing, etc. The learning process of ANN is one of the most labor-intensive stages in ANN. There are many different modifications of ANNs and methods for their training. Currently, deep neural networks are becoming one of the most popular methods of machine learning due to their effectiveness in areas such as speech recognition, medical informatics, computer vision, etc. It is known that ANN training depends on the type of input data. In this paper, reinforcement learning is considered, as popular method used in cases where information is reinforced by signals from the external environment with which the model interacts. The purpose of this paper is to develop a reinforcement meta-learning algorithm that would be efficient in terms of quality and speed of learning. However, despite the significant scientific progress in deep learning, existing algorithms are not efficient enough to solve problems in the real world. In addition, such algorithms require a significant amount of learning time, which complicates the development process. To solve these problems, the use of meta-learning or “learning to learn” algorithms has recently been especially relevant. The paper proposes an approach to reinforcement meta-learning using a multitasking weight optimizer. experimentally shown that the proposed approach is more efficient than the known MAML (Model-Agnostic Meta-Learning) algorithm. The proposed MAML SPSA-Track method shows an improvement in efficiency by an average of 4%, and MAML SPSA-Delta by 8%, respectively. Moreover, the last algorithm spends on average 2 times less time on push-v2 and pick-place-v2 tasks.

Keywords: Multitasking; meta-learning; reinforcement learning; neural networks; optimization

Ghazi Shakah, “Multi-Task Reinforcement Meta-Learning in Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130734

@article{Shakah2022,
title = {Multi-Task Reinforcement Meta-Learning in Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130734},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130734},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Ghazi Shakah}
}



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