Applications of Multi-Agent Deep Reinforcement Learning

Dear Colleagues,

Multi-Agent Deep Reinforcement Learning (MADRL) stands at the cutting edge of artificial intelligence, merging deep learning's perceptual power with reinforcement learning's decision-making capabilities to govern systems of multiple interactive agents. Fueled by breakthroughs in large language models (LLMs), decentralized coordination, and digital twins, MADRL is transcending theoretical boundaries to solve real-world complexities. It addresses core challenges—non-stationarity, partial observability, and scalability—to enable adaptive collaboration in dynamic environments. Current frontiers focus on LLM-agent hybrid architectures, graph-based communication, and robust distributed control, revolutionizing autonomous driving fleets, smart traffic management, intelligent robotics, network security, and smart grid optimization. This Special Issue explores state-of-the-art MADRL applications, bridging algorithmic innovation and industrial deployment to shape the next generation of collaborative intelligent systems.

Prof. Dr. Liangjun Ke
Prof. Dr. Xiaoyan Yin
Guest Editors

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