This study introduces the Instance-A}ware Index A}dvisor (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.

翻译：本研究提出了实例感知索引顾问（IA2），这是一种基于深度强化学习（DRL）的新方法，用于在面临庞大候选动作空间的数据库中优化索引选择。IA2引入了双延迟深度确定性策略梯度 - 时序差分状态智能精炼（TD3-TD-SWAR）模型，通过理解工作负载与索引之间的依赖关系并采用自适应动作掩码，实现了高效的索引选择。该方法包含一个全面的工作负载模型，增强了其对未见工作负载的适应能力，并确保在多样化数据库环境下保持稳健性能。在TPC-H等基准测试上的评估显示，IA2建议的索引在提升运行时性能方面表现出色：对于复杂的TPC-H工作负载，运行时性能相比无索引场景降低了40%，并且相比现有最先进的基于DRL的索引顾问性能提升了20%。