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Electronics
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Reinforcement Learning Meets Control: Theories and Applications
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Reinforcement Learning Meets Control: Theories and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2077

Special Issue Editors

Dr. Zezhi Tang

E-Mail Website
Guest Editor
Department of Computer Science, University College London, London WC1E 6BT, UK
Interests: reinforcement learning; machine learning; medical robotics; autonomous vehicle systems; autonomous agents and multiagent systems; neural networks; control engineering; intelligent robotics
Dr. Yi Dong

E-Mail Website
Guest Editor
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK
Interests: trustworthy AI; responsible AI; decision making/control; distributed optimization for autonomous systems
Dr. Yunda Yan

E-Mail Website
Guest Editor
Department of Computer Science, University College London, London WC1E 6BT, UK
Interests: model predictive control; reinforcement learning; aerial robotics; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zepeng Liu

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: digital signal processing; tool condition monitoring; fault diagnosis; power systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reinforcement Learning (RL) has become an innovative approach in control systems, providing the capacity to address complex, nonlinear, and dynamic environments where traditional control methods face limitations. Meanwhile, combining adaptive learning with traditional control techniques can address optimal problems in uncertain environments. This Special Issue focuses on the integration of RL and control, showcasing recent advancements that merge diverse RL methods with classical control frameworks, as RL's ability to learn optimal policies without requiring explicit system models provides a robust solution for complex control tasks across domains like robotics, process control, and autonomous systems. We welcome a wide range of publications, such as RL-based adaptive control, deep reinforcement learning in high-dimensional spaces, and the integration of RL with traditional control methods for improved adaptability and robustness. Based on that, we invite contributions that demonstrate novel approaches, theoretical insights, and practical applications of RL in control, showcasing how this integration enhances performance in real-world systems. Topics include:

  • Model-free RL for real-time control in complex systems.
  • RL with uncertainty compensation in adaptive systems.
  • Sample-efficient RL algorithms for constrained resources.
  • Safe RL for robust control in dynamic environments.
  • Transfer learning in RL for improved adaptability.
  • Multi-agent RL for cooperative decision-making.
  • Hybrid control systems combining RL.

Dr. Zezhi Tang
Dr. Yi Dong
Dr. Yunda Yan
Dr. Zepeng Liu
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

  • reinforcement learning
  • adaptive control
  • adaptive/approximate dynamic programming
  • robotics
  • deep learning
  • autonomous systems
  • optimal control

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Published Papers (2 papers)

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21 pages, 3426 KiB  
Article
A Safe Navigation Algorithm for Differential-Drive Mobile Robots by Using Fuzzy Logic Reward Function-Based Deep Reinforcement Learning
by Mustafa Can Bingol
Electronics 2025, 14(8), 1593; https://doi.org/10.3390/electronics14081593 - 15 Apr 2025
Viewed by 422
Abstract
Researchers are actively exploring advanced algorithms to enhance robots’ ability to navigate complex environments while avoiding obstacles. Four different environments were designed in the Webots simulator, including a mobile robot, a goal, a static obstacle, and one or two dynamic obstacles. The robot’s [...] Read more.
Researchers are actively exploring advanced algorithms to enhance robots’ ability to navigate complex environments while avoiding obstacles. Four different environments were designed in the Webots simulator, including a mobile robot, a goal, a static obstacle, and one or two dynamic obstacles. The robot’s state vector was determined based on its position, the goal, and sensor variables, with all elements randomly placed in each learning and test step. A multi-layer perceptron (MLP) agent was trained for 1000 episodes in these environments using classical and fuzzy logic-based reward functions. After the training process was completed, the agents trained with the fuzzy logic-based reward function were tested for each environment. As a result of the test, while the robot’s arrival rate was 100% in the first three environments, it was measured as 91% in the fourth environment. In the last environment, the rate of crashing into a wall or dynamic obstacle was observed to be 7%. In addition, the agent trained in the fourth environment was found to successfully reach the target in multi-robot environments. The agent trained fuzzy logic-based reward function obtained the best result for four different environments. Based on these results, a fuzzy logic-based reward function was proposed to address the tuning problem of the classical reward function. It was demonstrated that a robust fuzzy logic-based reward function was successfully designed. This study contributed to the literature by presenting a reinforcement learning-based safe navigation algorithm incorporating a fuzzy logic-based reward function. Full article
(This article belongs to the Special Issue Reinforcement Learning Meets Control: Theories and Applications)
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15 pages, 5609 KiB  
Article
Rapidly Exploring Random Trees Reinforcement Learning (RRT-RL): A New Era in Training Sample Diversity
by István Péter, Bálint Kővári and Tamás Bécsi
Electronics 2025, 14(3), 443; https://doi.org/10.3390/electronics14030443 - 22 Jan 2025
Viewed by 1324
Abstract
Sample efficiency is a crucial problem in Reinforcement Learning, especially when tackling environments with sparse reward signals that make convergence and learning cumbersome. In this work, a novel method is developed that combines Rapidly Exploring Random Trees with Reinforcement Learning to mitigate the [...] Read more.
Sample efficiency is a crucial problem in Reinforcement Learning, especially when tackling environments with sparse reward signals that make convergence and learning cumbersome. In this work, a novel method is developed that combines Rapidly Exploring Random Trees with Reinforcement Learning to mitigate the inefficiency of the trial-and-error-based experience-gathering concept through the systematic exploration of the state space. The combined approach eliminates the redundancy in irrelevant training samples. Consequently, the pivotal training signals, despite their sparsity, can be further exposed to support the learning process. Experiments are made on several OpenAI gym environments to demonstrate that the proposed method does not have any context-dependent components, and the results show that it can outperform the classic trial-and-error-based training approach. Full article
(This article belongs to the Special Issue Reinforcement Learning Meets Control: Theories and Applications)
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