Special Issue "Deep Reinforcement Learning: Methods and Applications"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Thanh Thi Nguyen
Website
Guest Editor
School of Information Technology, Deakin University, Burwood VIC 3125, Australia
Interests: artificial intelligence; deep learning; deep reinforcement learning; data science; big data; cyber security; IoT; image processing; robotics; autonomous vehicles; multi-agent systems; human-machine integration; defence technologies
Assoc. Prof. Dr. Peter Vamplew
Website
Guest Editor
Federation Learning Agents Group, Federation University Australia, Mount Helen 3350, Australia
Interests: multi-objective reinforcement learning; applications of reinforcement learning; AI safety; AI ethics

Special Issue Information

Dear Colleagues,

Real-world problems are increasingly complex, and applications of traditional reinforcement learning (RL) methods to solve these problems are becoming more and more challenging. Fortunately, deep learning has emerged as a powerful tool, and with the great capability of function approximation and representation learning, it is an excellent complement to traditional RL methods. The combination of deep learning and RL, namely deep RL, has made breakthroughs in developing artificial agents that can perform at human-level. Deep RL methods have been able to solve many complex problems in different domains from video games (e.g., Atari games, the game of Go, the real-time strategy game StarCraft II, the 3D multiplayer game Capture the Flag in Quake III Arena, and the teamwork game Dota 2) to real-world applications such as robotics, autonomous vehicles, autonomous surgery, biological data mining, drug design, cybersecurity, and the internet of things.

This Special Issue focuses on methods and applications of deep RL. We would like to invite papers proposing advanced deep RL methods and/or their novel applications to solve complex problems in various domains.

Dr. Thanh Thi Nguyen
Assoc. Prof. Peter Vamplew
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 papers will be 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 monthly 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 1500 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

  • reinforcement learning
  • deep learning
  • Deep Q-network
  • multiagent RL
  • multiobjective RL
  • autonomous vehicles
  • autonomy
  • robotics

Published Papers

This special issue is now open for submission.
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