Structure-Enhanced DRL for Optimal Transmission Scheduling

TL;DR

This paper introduces a structure-enhanced deep reinforcement learning framework for optimal transmission scheduling in remote state estimation, leveraging structural properties to improve learning efficiency and estimation accuracy.

Contribution

It develops a novel structure-enhanced DRL method that incorporates policy structure into action selection and loss functions, improving convergence and performance.

Findings

Training time reduced by 50%

Estimation MSE decreased by 10-25%

Structural properties applicable to various scheduling problems

Abstract

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25% when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.