docs: Add comprehensive multi-task learning architecture and gameplan

Browse Source

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.

...

This commit is contained in:

Claude

2025-12-11 09:51:41 +00:00

parent d45b344264

commit aab54ea7c0

2 changed files with 1999 additions and 0 deletions

Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL

Download Patch File Download Diff File Expand all files Collapse all files

1596

docs/GAMEPLAN_MULTITASK_PRICING.md Normal file

File diff suppressed because it is too large Load Diff