Reinforcement Learning, Physics-Informed Neural Networks, Intelligent Control, and Robotics
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 28
Editors
Interests: reinforcement learning; physics-informed neural networks; intelligent control; robotics; autonomous systems; robot learning; adaptive control; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; pattern recognition; machine learning; bioinformatics statistics mathematical modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Reinforcement learning, physics-informed neural networks (PINNs), intelligent control, and robotics are rapidly converging to reshape the design, optimization, and deployment of autonomous systems. With the development of deep reinforcement learning, model-based control, imitation learning, physics-informed learning, foundation models, and embodied intelligence, robots and intelligent systems are increasingly able to learn from interaction, incorporate physical constraints, adapt to uncertain environments, and make decisions under complex dynamical conditions. These advances are driving new opportunities in industrial automation, service robotics, autonomous vehicles, human–robot collaboration, smart manufacturing, healthcare robotics, and other real-world applications.
Despite these advances, many challenges remain in achieving reliable, safe, sample-efficient, physically consistent, and transferable learning and control. Key issues include the integration of learning-based methods with classical control theory and physical laws, safe exploration in physical systems, sim-to-real transfer, robustness under disturbances and uncertainty, real-time decision making, multi-agent coordination, and explainable or verifiable autonomous behavior. Addressing these challenges requires interdisciplinary research across artificial intelligence, control theory, robotics, optimization, scientific machine learning, perception, and embedded systems.
This Special Issue aims to present high-quality original research and review articles on recent advances in reinforcement learning, physics-informed neural networks, intelligent control, and robotics. Topics of interest include, but are not limited to, reinforcement learning algorithms, PINNs and scientific machine learning, robot learning, adaptive and optimal control, learning-based control, safe and robust autonomy, multi-agent reinforcement learning, human–robot interaction, autonomous navigation and manipulation, sim-to-real transfer, and applications of intelligent control in robotic and cyber-physical systems.
Dr. Binbin Yong
Dr. Ognjen Arandjelović
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 1800 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
- physics-informed neural networks
- scientific machine learning
- physics-informed control
- intelligent control
- robotics
- autonomous systems
- robot learning
- adaptive control
- safe reinforcement learning
- multi-agent systems
- human–robot interaction
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
