Deep Reinforcement Learning for Multiagent Systems

Dear Colleagues,

The field of Deep Reinforcement Learning for Multiagent Systems (DRL-MAS) is rapidly growing and dynamic, revolutionizing how autonomous agents learn and interact in complex environments. This area of research is pushing the boundaries of artificial intelligence, enabling the development of sophisticated, cooperative, and competitive behaviours among multiple agents across various real-world applications. This Special Issue aims to highlight and disseminate the latest advancements in DRL-MAS, with a specific focus on environments where multiple autonomous agents interact and make decisions. These interactions can be cooperative, competitive, or a mixture of both, posing unique challenges and opportunities for research and application. This Special Issue invites submissions that explore innovative algorithms advancing the frontier of DRL-MAS in multiagent scenarios. This encompasses, but is not limited to, novel approaches for policy learning, value function approximation, and exploration–exploitation strategies specifically designed for multiagent environments. Additionally, contributions that offer new theoretical insights into the behaviour, convergence, and optimality of DRL-MAS methods in multiagent environments are highly encouraged. Such theoretical foundations are crucial for understanding the strengths and limitations of existing approaches and for guiding the development of more robust and efficient algorithms. Practical implementations and case studies are also of great interest. These papers should demonstrate the application of DRL-MAS in real-world multiagent systems, such as robotic teams, autonomous driving fleets, smart grid management, financial markets, and complex gaming environments. These practical insights are valuable for showcasing the potential of DRL-MAS to solve real-world problems that involve multiple interacting agents.

Dr. Rakib Abdur
Dr. Mehmet Aydin
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.

• cooperative and competitive machine learning in multiagent systems
• cooperative multiagent reinforcement learning
• competitive multiagent reinforcement learning
• multiagent machine learning for policy fine-tune and optimization
• multiagent communication and coordination
• multiagent value function approximation
• transfer learning and generalization of experience
• decentralized control and decision-making in multiagent systems
• exploration and exploitation strategies in multiagent environments
• robustness and adaptability of DRL-MAS to dynamic environments
• theoretical foundations and analysis of DRL-MAS algorithms