Advancements and Applications in Reinforcement Learning

Dear Colleagues,

Reinforcement learning (RL) is rapidly transforming numerous industries and domains with real-world applications through its capacity to model and adapt learning based on interactions with the environment. This Special Issue (SI) aims to delve into the practical applications and advancements of RL, showcasing how these cutting-edge technologies are driving innovation and efficiency across various sectors.

This SI covers a wide range of topics, including, but not limited to, the following:

• RL in Robotics and Automation: Exploring how RL is enhancing robotic systems and automation processes.
• Game Playing and Simulations: Investigating the role of RL in developing sophisticated game-playing agents and realistic simulations.
• Recommendation Systems: Demonstrating the use of RL for creating personalized recommendation systems.
• Healthcare and Medicine: Highlighting RL applications in improving healthcare delivery and medical decision making.
• Multi-Agent Reinforcement Learning: Discussing collaborative systems and the coordination of multiple agents using RL.
• Autonomous Vehicles and Transportation: Examining how RL is advancing the development of self-driving cars and optimizing transportation systems.
• Adaptive Control and Scheduling: Presenting RL approaches for dynamic control systems and efficient scheduling.
• Adaptive User Interfaces and Human–Computer Interactions: Showcasing RL in the design of responsive user interfaces and enhancing user experiences.
• Crowd Simulation and Management: Investigating the use of RL to model and simulate crowd behaviors, enhancing safety and efficiency in public spaces and events.
• Realistic Behaviors Modeling: Exploring how RL can be employed to generate realistic human-like behaviors through virtual agents.

We invite submissions that present novel RL algorithms, theoretical advancements, case studies, experimental evaluations, and comprehensive reviews summarizing recent developments. Contributions can range from original research papers to surveys and tutorials, aiming to foster a thorough understanding of RL's real-world impact and potential across various domains.

This Special Issue will serve as a platform to highlight high-quality, original research and innovative ideas, encouraging the exchange of knowledge and the advancement of RL technologies.

Dr. Chairi Kiourt
Prof. Dr. Dimitris Kalles
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. Applied Sciences 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.

• reinforcement learning
• adaptive control
• collaborative learning
• robotics and automation
• multi-agent systems
• simulation
• decision making
• non-stationary environment.