The Crucial Role of Problem Formulation in Real-World Reinforcement Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach
Online decision making plays a crucial role in numerous real-world applications. In many scenarios, the decision is made based on performing a sequence of tests on the incoming data points. However, performing all tests …
Decision MakingMulti-Armed BanditsThompson SamplingOn the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Motivated from real world problems, like object categorization, we study a particular mixed-norm regularization for Multiple Kernel Learning (MKL). It is assumed that the given set of kernels are grouped into distinct co…
Object CategorizationRobust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained…
ManagementReinforcement Learning (RL)Autoformulation of Mathematical Optimization Models Using LLMs
Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a formidable challenge, of…
Decision MakingAn Introduction to Cognidynamics
This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-worl…
Dynamic neural networks