# Deep reinforcement learning for scheduling in large-scale networked control systems

## TL;DR

This paper introduces DIRA, a deep reinforcement learning algorithm designed for scalable, control-aware resource scheduling in large-scale networked control systems, optimizing joint control and scheduling performance.

## Contribution

The paper presents DIRA, a novel deep reinforcement learning approach tailored for large-scale, control-aware resource scheduling in networked systems, especially with correlated fading channels.

## Key findings

- DIRA scales effectively to large scheduling problems.
- DIRA improves control performance in networked systems.
- Simulation results validate DIRA's scalability and effectiveness.

## Abstract

This work considers the problem of control and resource scheduling in networked systems. We present DIRA, a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems.