# Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments

arXiv: https://arxiv.org/abs/2411.05346 · HTML: https://mlsanity.com/paper/2411.05346/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm](https://mlsanity.com/paper/2410.12259/) (2024)
- [Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm](https://mlsanity.com/paper/2410.17586/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment](https://mlsanity.com/paper/2409.17909/) (2024)
- [Automated Genre-Aware Article Scoring and Feedback Using Large Language Models](https://mlsanity.com/paper/2410.14165/) (2024)
- [Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems](https://mlsanity.com/paper/2410.14167/) (2024)

## Built on by (most cited first, 7 of 7)

- [Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs](https://mlsanity.com/paper/2411.15195/) (2024)
- [Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches](https://mlsanity.com/paper/2411.16567/) (2024)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation](https://mlsanity.com/paper/2411.12157/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervision](https://mlsanity.com/paper/2412.15222/) (2024)
- [Slim Scheduler: A Runtime-Aware RL and Scheduler System for Efficient CNN Inference](https://mlsanity.com/paper/2510.09018/) (2025)

## Abstract (arXiv)

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin and Priority Scheduling fail to meet the demands of efficient resource allocation and real-time adaptability. By contrast, Q-learning, a reinforcement learning algorithm, continuously learns from system state changes, enabling dynamic scheduling and resource optimization. Through extensive experiments, the superiority of the proposed approach is demonstrated in both task completion time and resource utilization, outperforming traditional and dynamic resource allocation (DRA) algorithms. These findings are critical as they highlight the potential of intelligent scheduling algorithms based on reinforcement learning to address the growing complexity and unpredictability of computing environments. This research provides a foundation for the integration of AI-driven adaptive scheduling in future large-scale systems, offering a scalable, intelligent solution to enhance system performance, reduce operating costs, and support sustainable energy consumption. The broad applicability of this approach makes it a promising candidate for next-generation computing frameworks, such as edge computing, cloud computing, and the Internet of Things.