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Electronics
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Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-Learning
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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 (19 August 2022) | Viewed by 4294

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 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 We invite original and unpublished submissions that feature innovative methods for enhancing modeling, learning and testing, dataset creation and processing, and the utilization of deep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 the internet of things
  • machine learning for computer and network systems
  • machine learning for privacy

Published Papers (2 papers)

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Research

14 pages, 630 KiB  
Article
An Improved Multi-Objective Deep Reinforcement Learning Algorithm Based on Envelope Update
by Can Hu, Zhengwei Zhu, Lijia Wang, Chenyang Zhu and Yanfei Yang
Electronics 2022, 11(16), 2479; https://doi.org/10.3390/electronics11162479 - 9 Aug 2022
Viewed by 2208
Abstract
Multi-objective reinforcement learning (MORL) aims to uniformly approximate the Pareto frontier in multi-objective decision-making problems, which suffers from insufficient exploration and unstable convergence. We propose a multi-objective deep reinforcement learning algorithm (envelope with dueling structure, Noisynet, and soft update (EDNs)) to improve the [...] Read more.
Multi-objective reinforcement learning (MORL) aims to uniformly approximate the Pareto frontier in multi-objective decision-making problems, which suffers from insufficient exploration and unstable convergence. We propose a multi-objective deep reinforcement learning algorithm (envelope with dueling structure, Noisynet, and soft update (EDNs)) to improve the ability of the agent to learn optimal multi-objective strategies. Firstly, the EDNs algorithm uses neural networks to approximate the value function and update the parameters based on the convex envelope of the solution boundary. Then, the DQN structure is replaced with the dueling structure, and the state value function is split into the dominance function and value function to make it converge faster. Secondly, the Noisynet method is used to add exploration noise to the neural network parameters to make the agent have a more efficient exploration ability. Finally, the soft update method updates the target network parameters to stabilize the training procedure. We use the DST environment as a case study, and the experimental results show that the EDNs algorithm has better stability and exploration capability than the EMODRL algorithm. In 1000 episodes, the EDNs algorithm improved the coverage by 5.39% and reduced the adaptation error by 36.87%. Full article
(This article belongs to the Special Issue Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-Learning)
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15 pages, 1002 KiB  
Article
Analysis of Mobile Robot Control by Reinforcement Learning Algorithm
by Jakub Bernat, Paweł Czopek and Szymon Bartosik
Electronics 2022, 11(11), 1754; https://doi.org/10.3390/electronics11111754 - 31 May 2022
Cited by 2 | Viewed by 1424
Abstract
This work presents a Deep Reinforcement Learning algorithm to control a differentially driven mobile robot. This study seeks to explain the influence of different definitions of the environment with a mobile robot on the learning process. In our study, we focus on the [...] Read more.
This work presents a Deep Reinforcement Learning algorithm to control a differentially driven mobile robot. This study seeks to explain the influence of different definitions of the environment with a mobile robot on the learning process. In our study, we focus on the Reinforcement Learning algorithm called Deep Deterministic Policy Gradient, which is applicable to continuous action problems. We investigate the effectiveness of different noises, inputs, and cost functions in the neural network learning process. To examine the feature of the presented algorithm, a number of simulations were run, and their results are presented. In the simulations, the mobile robot had to reach a target position in a way that minimizes distance error. Our goal was to optimize the learning process. By analyzing the results, we wanted to recommend a more efficient choice of input and cost functions for future research. Full article
(This article belongs to the Special Issue Machine Learning Technologies: Deep Learning, Reinforcement Learning and Q-Learning)
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