Joint Optimization of Spatiotemporal Task Scheduling and Radio Resource Allocation in Agentic Wireless Control Systems

Abstract

Smart factories are evolving into agentic control systems powered by wireless connectivity, edge computing, and artificial intelligence. This evolution alleviates computational limits and enhances production efficiency. However, the heterogeneity of spatiotemporal control logic, coupled with indeterminate wireless conditions, makes it challenging to coordinate control tasks and radio resources. To overcome these challenges, this paper presents a mixed graph-driven model to characterize spatiotemporal dependencies among control tasks and proposes a semi-centralized multi-agent collaborative framework. This paper employs an improved heterogeneous twin delayed deep deterministic policy gradient algorithm to jointly optimize task scheduling and radio resource allocation, thereby minimizing the average processing delay of industrial control processes. Simulation results demonstrate that the proposed algorithm achieves outstanding performance compared to benchmarks, improving execution success rate and data processing rate, as well as reducing model training time.