Reinforcement Learning: Emerging Techniques and Future Prospects

Dear Colleagues,

Reinforcement learning (RL) has emerged as a powerful paradigm for sequential decision-making, enabling agents to learn optimal behaviors through interaction with dynamic and uncertain environments. In recent years, RL has made remarkable progress and found widespread applications across various domains such as robotics, wireless communications, autonomous systems, intelligent control, and industrial optimization. However, many challenges remain, including sample inefficiency, exploration–exploitation trade-offs, scalability to large state-action spaces, safety guarantees, and coordination among multiple agents.

This Special Issue aims to provide a platform for researchers and practitioners to present the latest advancements in reinforcement learning, covering both theoretical foundations and practical applications. We especially welcome contributions that explore novel algorithms, frameworks, and system designs that address key limitations in current RL approaches, as well as emerging trends such as offline RL, safe RL, multi-agent RL, and federated or privacy-preserving RL. Interdisciplinary works that integrate RL with areas like digital twin, network optimization, edge computing, and intelligent sensing are particularly encouraged.

Topics of interest include, but are not limited to, the following:

• Deep reinforcement learning and its theoretical analysis;
• Model-based and model-free RL algorithms;
• Multi-agent reinforcement learning and coordination;
• RL in wireless networks, edge/cloud systems, and IoT;
• Sample-efficient, robust, or safe RL approaches;
• Federated RL and privacy-preserving learning;
• RL applications in robotics, smart manufacturing, and control systems.

Dr. Haoqiang Liu
Dr. Wenzhen Huang
Dr. Huiming Chen
Guest Editors

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