# Learning Resource Scheduling with High Priority Users using Deep Deterministic Policy Gradients

arXiv: https://arxiv.org/abs/2304.09488 · HTML: https://mlsanity.com/paper/2304.09488/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer](https://mlsanity.com/paper/2003.11003/) (2020)
- [Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation](https://mlsanity.com/paper/2009.08346/) (2020)
- [Deep Reinforcement Model Selection for Communications Resource Allocation in On-Site Medical Care](https://mlsanity.com/paper/2111.06680/) (2021)

## Built on by (most cited first, 2 of 2)

- [Robust Deep Reinforcement Learning Scheduling via Weight Anchoring](https://mlsanity.com/paper/2304.10176/) (2023)
- [A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation](https://mlsanity.com/paper/2304.12660/) (2023)

## Abstract (arXiv)

Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled to guide on-site paramedics and can, in turn, be supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and efficient, yet easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient~(ddpg) methods for learning a communications resource scheduling algorithm with special regards to priority users. Unlike the popular Deep-Q-Network methods, the ddpg is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.