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aab54ea7c0
Created detailed documentation for implementing multi-task learning system to improve agent detection and dynamic pricing: - GAMEPLAN_MULTITASK_PRICING.md: Complete 50+ page technical specification including feature engineering, supervised learning, multi-task neural networks, synthetic simulator, and knowledge distillation approach - ARCHITECTURE_OVERVIEW.md: Quick reference with visual diagrams comparing current rule-based system to proposed ML architecture, metrics, and implementation phases Key improvements proposed: - Replace O(n²) SessionState pipeline with vectorized feature extraction - Train XGBoost classifier on experimentId labels (ROC-AUC >0.90 target) - Multi-task neural network for joint agent detection + purchase prediction - Gymnasium-based synthetic pricing environment for safe experimentation - Knowledge distillation to extract interpretable pricing heuristics Addresses margin leakage concerns with learned pricing strategies instead of simple velocity thresholds.
