mlsanityData to 7 Oct 2026

A Survey on Deep Reinforcement Learning for Data Processing and Analytics

arXiv 2108.04526 ↗First posted 10 Aug 2021cs.LG cs.AI cs.DBcited by 10

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Abstract (arXiv)

Data processing and analytics are fundamental and pervasive. Algorithms play a vital role in data processing and analytics where many algorithm designs have incorporated heuristics and general rules from human knowledge and experience to improve their effectiveness. Recently, reinforcement learning, deep reinforcement learning (DRL) in particular, is increasingly explored and exploited in many areas because it can learn better strategies in complicated environments it is interacting with than statically designed algorithms. Motivated by this trend, we provide a comprehensive review of recent works focusing on utilizing DRL to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in DRL. Next, we discuss DRL deployment on database systems, facilitating data processing and analytics in various aspects, including data organization, scheduling, tuning, and indexing. Then, we survey the application of DRL in data processing and analytics, ranging from data preparation, natural language processing to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using DRL in data processing and analytics.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 68 of its 132 references that are in the map. First posted on arXiv 10 Aug 2021.

Builds on

68 of 132 references.

References · 68

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38 more
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50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 3; 2026: 1.

10 papers.

  1. 2023Systematic Review on Reinforcement Learning in the Field of Fintechcited by 2
  2. 2023Robot path planning using deep reinforcement learningcited by 1
  3. 2025LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Constructioncited by 1
  4. 2022The Metaverse Data Deluge: What Can We Do About It?cited by 0
  5. 2024User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAUcited by 0
  6. 2024Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptationcited by 0
  7. 2024Data Acquisition for Improving Model Fairness using Reinforcement Learningcited by 0
  8. 2025Two-Stage Feature Generation with Transformer and Reinforcement Learningcited by 0
  9. 2025Joint AoI and Handover Optimization in Space-Air-Ground Integrated Networkcited by 0
  10. 2026Improving Generalization and Robustness in Offline Reinforcement Learning via Boundary-Aware Data Augmentationcited by 0