Learning State Representations for Query Optimization with Deep Reinforcement Learning

被引：52

作者：

Ortiz, Jennifer ^{[1
]}

Balazinska, Magdalena ^{[1
]}

Gehrke, Johannes ^{[2
]}

Keerthi, S. Sathiya ^{[3
]}

机构：

[1] Univ Washington, Seattle, WA 98195 USA

[2] Microsoft, Redmond, WA USA

[3] Criteo Res, Ann Arbor, MI USA

来源：

PROCEEDINGS OF THE SECOND WORKSHOP ON DATA MANAGEMENT FOR END-TO-END MACHINE LEARNING | 2018年

关键词：

D O I：

10.1145/3209889.3209890

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We explore the idea of using deep reinforcement learning for query optimization. The approach is to build queries incrementally by encoding properties of subqueries using a learned representation. In this paper, we focus specifically on the state representation problem and the formation of the state transition function. We show preliminary results and discuss how we can use the state representation to improve query optimization using reinforcement learning.

引用

页数：4

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