Optimization of Reinforcement Learning Algorithms for Addressing the Linear Function Fitting Challenge in the Electric Power Internet of Things

Abstract

Abstract With the rapid development of the Electric Power Internet of Things (EPIoT), effectively utilizing reinforcement learning algorithms for optimizing energy management and distribution has become crucial. This paper aims to explore and address one of the primary challenges when applying reinforcement learning algorithms in the context of EPIoT: the issue of linear function fitting. Firstly, we analyze the complexity of data processing in the EPIoT and why standard reinforcement learning algorithms perform poorly in this environment. Subsequently, this research proposes an improved reinforcement learning framework that enhances the accuracy and efficiency of the algorithm in handling large-scale, high-dimensional data by optimizing the linear function fitting process. We validate the performance of the proposed algorithm through a series of experiments using real-world power data. The experimental results demonstrate significant improvements in accuracy and computational speed compared to traditional methods. Finally, this paper discusses the limitations of the study and future research directions, providing new perspectives and ideas for further research in the Electric Power Internet of Things.