mlsanityData to 14 Oct 2026

IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads

arXiv 2404.05777 ↗First posted 8 Apr 2024cs.DB cs.AIcited by 2

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

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. IA2 introduces the Twin Delayed Deep Deterministic Policy Gradient - Temporal Difference State-Wise Action Refinery (TD3-TD-SWAR) model, enabling efficient index selection by understanding workload-index dependencies and employing adaptive action masking. This method includes a comprehensive workload model, enhancing its ability to adapt to unseen workloads and ensuring robust performance across diverse database environments. Evaluation on benchmarks such as TPC-H reveals IA2's suggested indexes' performance in enhancing runtime, securing a 40% reduction in runtime for complex TPC-H workloads compared to scenarios without indexes, and delivering a 20% improvement over existing state-of-the-art DRL-based index advisors.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 16 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

2 of 16 references.

References · 2

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2022Toward Causal-Aware RL: State-Wise Action-Refined Temporal Differencecited by 6

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Citing papers per year: 2024: 2.

2 papers.

  1. 2024DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agentscited by 68
  2. 2024OCMDP: Observation-Constrained Markov Decision Processcited by 2

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