# DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling

arXiv: https://arxiv.org/abs/2105.07526 · HTML: https://mlsanity.com/paper/2105.07526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Deep Reinforcement Agent for Scheduling in HPC](https://mlsanity.com/paper/2102.06243/) (2021)
- [Scheduling Beyond CPUs for HPC](https://mlsanity.com/paper/2012.05439/) (2020)

## Built on by (most cited first, 3 of 3)

- [QAISim: A Toolkit for Modeling and Simulation of AI in Quantum Cloud Computing Environments](https://mlsanity.com/paper/2512.17918/) (2025)
- [Attention-Informed Surrogates for Navigating Power-Performance Trade-offs in HPC](https://mlsanity.com/paper/2601.15399/) (2026)
- [What Artificial Intelligence can do for High-Performance Computing systems?](https://mlsanity.com/paper/2602.00014/) (2026)

## Abstract (arXiv)

For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems. However, the increasingly complex HPC systems combined with highly diverse workloads make such manual process challenging, time-consuming, and error-prone. We present a reinforcement learning based HPC scheduling framework named DRAS-CQSim to automatically learn optimal scheduling policy. DRAS-CQSim encapsulates simulation environments, agents, hyperparameter tuning options, and different reinforcement learning algorithms, which allows the system administrators to quickly obtain customized scheduling policies.