Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Trends and Prospects in Machine Learning Technologies: Deep Learning, Reinforcement...
share announcement

Trends and Prospects in Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (11 August 2023) | Viewed by 1335

Special Issue Editors

Dr. Eunjung Choi

E-Mail Website
Guest Editor
Department of Information Security, Seoul Women’s University, Seoul 01797, Republic of Korea
Interests: artificial intelligence; cybersecurity; malware; privacy; OSINT
Special Issues, Collections and Topics in MDPI journals
Dr. Jiyeon Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
Interests: artificial intelligence; cybersecurity; digital twin; cloud and IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning technology is contributing to technological development such as robots, autonomous driving, sound recognition, and prediction, starting with computer vision and pattern recognition. In particular, deep learning technology is improving and expanding to Reinforcement Learning and Q-Learning.

This Special Issue aims to publish original research of the highest scientific quality related to the trends and prospects in Deep Learning, Reinforcement Learning, and Q-Learning, the latest research trends in machine learning technology. We invite original and unpublished submissions that feature innovative methods for enhancing modeling, learning and testing, data set creation and processing, and utilization of Deep Learning, Reinforcement Learning, and Q-Learning.

The scope includes theoretical and experimental studies that contribute to novel developments in fundamental research and its applications.

Dr. Eunjung Choi
Dr. Jiyeon Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • reinforcement learning
  • Q-learning
  • machine learning for cybersecurity
  • machine learning for Internet of Things
  • machine learning for computer and network systems
  • machine learning for privacy

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 2126 KiB  
Article
Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning
by Yuangang Li, Tao Guo, Qinghua Li and Xinyue Liu
Electronics 2023, 12(16), 3508; https://doi.org/10.3390/electronics12163508 - 18 Aug 2023
Viewed by 770
Abstract
In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environment, [...] Read more.
In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environment, we present an innovative approach by introducing an intricately honed feature extraction module to harness the predictive errors, generate intrinsic rewards, and use an ancillary agent training paradigm that effectively solves the above problems and significantly enhances the agent’s capacity for comprehensive exploration within environments characterized by sparse reward distribution. The efficacy of the optimized feature extraction module is substantiated through comparative experiments conducted within the arduous exploration problem scenarios often employed in reinforcement learning investigations. Furthermore, a comprehensive performance analysis of our method is executed within the esteemed Atari 2600 experimental setting, yielding noteworthy advancements in performance and showcasing the attainment of superior outcomes in six selected experimental environments. Full article
(This article belongs to the Special Issue Trends and Prospects in Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-learning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop