# Join Query Optimization with Deep Reinforcement Learning Algorithms

arXiv: https://arxiv.org/abs/1911.11689 · HTML: https://mlsanity.com/paper/1911.11689/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

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

- [A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration](https://mlsanity.com/paper/2101.01507/) (2021)
- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [Autonomous Data Agents: A New Opportunity for Smart Data](https://mlsanity.com/paper/2509.18710/) (2025)
- [A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning](https://mlsanity.com/paper/2303.05710/) (2023)
- [GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints](https://mlsanity.com/paper/2411.04525/) (2024)
- [Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing](https://mlsanity.com/paper/2107.10508/) (2021)
- [OntoTune: Ontology-Driven Learning for Query Optimization with Convolutional Models](https://mlsanity.com/paper/2511.06780/) (2025)

## Abstract (arXiv)

Join query optimization is a complex task and is central to the performance of query processing. In fact it belongs to the class of NP-hard problems. Traditional query optimizers use dynamic programming (DP) methods combined with a set of rules and restrictions to avoid exhaustive enumeration of all possible join orders. However, DP methods are very resource intensive. Moreover, given simplifying assumptions of attribute independence, traditional query optimizers rely on erroneous cost estimations, which can lead to suboptimal query plans. Recent success of deep reinforcement learning (DRL) creates new opportunities for the field of query optimization to tackle the above-mentioned problems. In this paper, we present our DRL-based Fully Observed Optimizer (FOOP) which is a generic query optimization framework that enables plugging in different machine learning algorithms. The main idea of FOOP is to use a data-adaptive learning query optimizer that avoids exhaustive enumerations of join orders and is thus significantly faster than traditional approaches based on dynamic programming. In particular, we evaluate various DRL-algorithms and show that Proximal Policy Optimization significantly outperforms Q-learning based algorithms. Finally we demonstrate how ensemble learning techniques combined with DRL can further improve the query optimizer.