微调问题定义可显著提升强化学习在工业系统中的表现。
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning
- 通过优化问题表述设计，提升训练效率与稳定性。
- 在1-自由度直升机上实现更优的学习速度与最终策略性能。
- 适合关注工业强化学习落地的研究者与工程师。
强化学习（RL）为工业网络物理系统（ICPSs）的控制任务提供了有前景的解决方案，但其实际应用仍受限。本文表明，看似微小却精心设计的问题表述修改，可显著提升性能、稳定性和样本效率。我们识别并研究了RL问题表述的关键要素，发现这些要素能同时加快学习速度并提升最终策略质量。实验采用具有非线性动力学特性的1-自由度（1-DoF）直升机测试平台Quanser Aero²，该平台代表多种工业场景。仿真结果显示，所提问题设计原则带来更可靠高效的训练过程；进一步在真实硬件上训练验证，结果令人鼓舞。这表明，当细致关注问题表述设计原则时，强化学习在工业系统中具有巨大潜力。本研究强调，精心设计问题表述是弥合强化学习研究与现实工业需求之间差距的关键。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) offers promising solutions for control tasks in industrial cyber-physical systems (ICPSs), yet its real-world adoption remains limited. This paper demonstrates how seemingly small but well-designed modifications to the RL problem formulation can substantially improve performance, stability, and sample efficiency. We identify and investigate key elements of RL problem formulation and show that these enhance both learning speed and final policy quality. Our experiments use a one-degree-of-freedom (1-DoF) helicopter testbed, the Quanser Aero~2, which features non-linear dynamics representative of many industrial settings. In simulation, the proposed problem design principles yield more reliable and efficient training, and we further validate these results by training the agent directly on physical hardware. The encouraging real-world outcomes highlight the potential of RL for ICPS, especially when careful attention is paid to the design principles of problem formulation. Overall, our study underscores the crucial role of thoughtful problem formulation in bridging the gap between RL research and the demands of real-world industrial systems.
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