Special Issue "Advances in Reinforcement Learning"

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990). This special issue belongs to the section "Learning".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editor

Prof. Dr. Ausif Mahmood
Guest Editor
Chair, Department of Computer Science and Engineering, Professor of Computer Science and Engineering, and Electrical Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Interests: deep learning; reinforcement learning; computer vision; NLP; optimization

Special Issue Information

Dear Colleagues,

Reinforcement Learning (RL), in which the agents learn by interacting with the environment, is one of the most exciting areas of Artificial Intelligence. Unlike other AI paradigms of supervised and unsupervised learning, no predisposed intuition, data, or supervision is necessary in RL. Starting from the foundational work of the Bellman equations, many RL algorithms have been proposed in the last few years, and the success of RL has been demonstrated in many practical applications in the fields of robotics, autonomous vehicles, communication systems, game playing, finance, healthcare, adaptive decision control, among others. Even though considerable work on algorithmic and mathematical formulations related to single-agent RL systems has led to impressive results in different domains, Multi-Agent RL (MARL) is still in its infancy. Some of the challenges yet to be resolved, both for single- and Multi-Agent RL systems, include real-time adaptation to nonstationary or stochastic environments, high-dimensional continuous state and action spaces, adversarial RL including both attacks and defenses, partial observability of the environment, RL under interference or noisy environments, and safety control. To provide some of the solutions to the challenging problems of RL, we propose this Special Issue on “Advances in Reinforcement Learning”. With this aim, we invite papers in both theoretical and applied research areas related to RL and MARL. We believe this Special Issue will contribute to advancing the state of the art in reinforcement learning.

Prof. Dr. Ausif Mahmood
Guest Editor

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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1200 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.


  • reinforcement learning
  • multi-agent reinforcement learning
  • Markov decision process
  • value iteration
  • policy gradients
  • learning to learn
  • deep Q learning
  • Markov game
  • deep reinforcement learning

Published Papers

This special issue is now open for submission.
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