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Found 3 papers, 1 papers with code
IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads
This study introduces the Instance-Aware Index Advisor (IA2), a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases facing large action spaces of potential candidates.
Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference
Although it is well known that exploration plays a key role in Reinforcement Learning (RL), prevailing exploration strategies for continuous control tasks in RL are mainly based on naive isotropic Gaussian noise regardless of the causality relationship between action space and the task and consider all dimensions of actions equally important.
COAX: Correlation-Aware Indexing on Multidimensional Data with Soft Functional Dependencies
The initial work on learned indexes has shown that by learning the cumulative distribution function of the data, index structures such as the B-Tree can improve their performance by one order of magnitude while having a smaller memory footprint.
