# Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques

arXiv: https://arxiv.org/abs/2501.00068 · HTML: https://mlsanity.com/paper/2501.00068/

## Key facts

- Cited by 1 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 3 of its 17 references that are in the map.
- First posted on arXiv 29 Dec 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, 3 of 3)

- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles](https://mlsanity.com/paper/2406.04821/) (2024)
- [Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System](https://mlsanity.com/paper/2407.02759/) (2024)

## Built on by (most cited first, 1 of 1)

- [ML-driven detection and reduction of ballast information in multi-modal datasets](https://mlsanity.com/paper/2602.16876/) (2026)

## Abstract (arXiv)

The exponential growth of data-intensive applications has placed unprecedented demands on modern storage systems, necessitating dynamic and efficient optimization strategies. Traditional heuristics employed for storage performance optimization often fail to adapt to the variability and complexity of contemporary workloads, leading to significant performance bottlenecks and resource inefficiencies. To address these challenges, this paper introduces RL-Storage, a novel reinforcement learning (RL)-based framework designed to dynamically optimize storage system configurations. RL-Storage leverages deep Q-learning algorithms to continuously learn from real-time I/O patterns and predict optimal storage parameters, such as cache size, queue depths, and readahead settings[1].This work underscores the transformative potential of reinforcement learning techniques in addressing the dynamic nature of modern storage systems. By autonomously adapting to workload variations in real time, RL-Storage provides a robust and scalable solution for optimizing storage performance, paving the way for next-generation intelligent storage infrastructures.