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.

翻译：本研究提出了实例感知索引顾问（Instance-Aware Index Advisor, IA2），这是一种基于深度强化学习的新型方法，用于优化大规模候选索引空间下的数据库索引选择问题。IA2引入了双延迟深度确定性策略梯度-时序差分状态感知动作精炼（TD3-TD-SWAR）模型，通过理解工作负载与索引间的依赖关系并采用自适应动作掩码机制，实现高效的索引选择。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，并在多样化数据库环境中确保鲁棒性能。在TPC-H等基准测试上的评估表明，IA2推荐的索引在运行时性能提升方面效果显著：与无索引场景相比，针对复杂TPC-H工作负载的运行时降低40%，较现有最先进的基于深度强化学习的索引顾问提升20%。