  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.
