Reinforcement Learning for Real-World Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 115
Special Issue Editors
Interests: machine learning; artificial intelligence; complex system modeling and optimization; fault diagnosis and safety assessment
Special Issue Information
Dear Colleagues,
In recent years, reinforcement learning (RL) has garnered significant attention due to its outstanding performance in solving complex real-world problems. As a machine learning method that trains agents to make optimal decisions through interaction with dynamic environments, RL has demonstrated tremendous potential across multiple disciplines and industries, including robotics control, scheduling optimization, smart manufacturing, intelligent transportation, and energy management. This Special Issue aims to systematically investigate innovative practices of RL algorithms and their integration with other cutting-edge technologies, thereby providing new approaches to address diverse real-world challenges.
Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- RL for Robotics Control and Manipulator Operations: Applying RL to enhance the precision and efficiency of robotic systems, enabling autonomous navigation, manipulation, and task execution in complex environments.
- RL for Scheduling Optimization: Leveraging RL for efficient task allocation and job scheduling in dynamic settings such as manufacturing, logistics, and cloud computing.
- RL for Controller Optimization in Process Engineering: Using RL to optimize control strategies in chemical plants, improving process efficiency, safety, and resource utilization.
- RL Applications of Large Models: Exploring the integration and application of RL in large pre-trained models to enhance decision-making capabilities and generalization performance in complex tasks.
- RL for Intelligent Transportation Systems: Investigating the role of RL in optimizing traffic flow, route planning, and autonomous vehicle operations to improve mobility and reduce congestion.
- RL for Energy Management: Applying RL to intelligently manage energy resources, including demand forecasting, load balancing, and renewable energy integration.
Contributions are encouraged to present both theoretical insights and practical implementations, offering valuable perspectives for future research and technological innovation.
Prof. Dr. Dazi Li
Dr. Pablo Chamoso
Guest Editors
Manuscript Submission Information
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Keywords
- reinforcement learning
- robotics control
- scheduling optimization
- smart manufacturing
- intelligent transportation
- energy management
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