Instead of exhaustively listing all possible database indexes, the IA2 system uses a smarter approach. It employs validation rules, permutations, and heuristics to generate a refined set of high-potential index candidates. This creates a more focused and relevant "action space" for the reinforcement learning agent to explore, leading to more efficient training and better index selection.
IA2's preprocessing creates a rich workload model for its deep reinforcement learning task. This model doesn't just analyze queries; it integrates query plans, current indexes, database metadata, and tokenized queries. This holistic state representation is key to its ability to generalize across diverse database workloads, providing a more accurate view of the system's state.