# DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

arXiv: https://arxiv.org/abs/1803.02998 · HTML: https://mlsanity.com/paper/1803.02998/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Structure-Enhanced DRL for Optimal Transmission Scheduling](https://mlsanity.com/paper/2212.12704/) (2022)
- [Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control](https://mlsanity.com/paper/2109.12562/) (2021)
- [A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation](https://mlsanity.com/paper/2205.07070/) (2022)
- [Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model](https://mlsanity.com/paper/2207.09951/) (2022)
- [Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications](https://mlsanity.com/paper/2108.01317/) (2021)
- [GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits](https://mlsanity.com/paper/2408.09882/) (2024)
- [DISCOVER: Identifying Patterns of Daily Living in Human Activities from Smart Home Data](https://mlsanity.com/paper/2503.01733/) (2025)
- [Adaptive Scheduling: A Reinforcement Learning Whittle Index Approach for Wireless Sensor Networks](https://mlsanity.com/paper/2601.01179/) (2026)
- [Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning](https://mlsanity.com/paper/2605.10482/) (2026)

## Abstract (arXiv)

We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this paper is to develop a deep reinforcement learning-based control-aware scheduling (textscDeepCAS) algorithm to tackle these issues. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design a learning algorithm that adapts to the chosen subsystems (plants) and controllers. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the control loss. We present empirical results to show that textscDeepCAS finds schedules with better performance than periodic ones.