Reinforcement Learning Meets Control: Theories and Applications

Dear Colleagues,

Reinforcement Learning (RL) has become an innovative approach in control systems, providing the capacity to address complex, nonlinear, and dynamic environments where traditional control methods face limitations. Meanwhile, combining adaptive learning with traditional control techniques can address optimal problems in uncertain environments. This Special Issue focuses on the integration of RL and control, showcasing recent advancements that merge diverse RL methods with classical control frameworks, as RL's ability to learn optimal policies without requiring explicit system models provides a robust solution for complex control tasks across domains like robotics, process control, and autonomous systems. We welcome a wide range of publications, such as RL-based adaptive control, deep reinforcement learning in high-dimensional spaces, and the integration of RL with traditional control methods for improved adaptability and robustness. Based on that, we invite contributions that demonstrate novel approaches, theoretical insights, and practical applications of RL in control, showcasing how this integration enhances performance in real-world systems. Topics include:

• Model-free RL for real-time control in complex systems.
• RL with uncertainty compensation in adaptive systems.
• Sample-efficient RL algorithms for constrained resources.
• Safe RL for robust control in dynamic environments.
• Transfer learning in RL for improved adaptability.
• Multi-agent RL for cooperative decision-making.
• Hybrid control systems combining RL.

Dr. Zezhi Tang
Dr. Yi Dong
Dr. Yunda Yan
Dr. Zepeng Liu
Guest Editors

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