Phase 1 generalization techniques to close IS/OOS Sharpe gap (+0.57→-1.30).
All techniques fully wired from DQNHyperparameters → TOML [generalization]
section → ExperienceCollectorConfig → CUDA kernel arguments.
New techniques implemented:
- #13 Vol normalization: divide return features by realized vol (state_gather)
- #18 Asymmetric DD loss: extra penalty on Q-overestimation in drawdown
(mse_loss_batched + c51_loss_batched)
- #22 Feature noise: N(0, scale) per feature via LCG RNG (state_gather)
- #23 Causal feature masking: random 30% feature subset zeroed per epoch
(state_gather, mask uploaded from Rust)
- #24 Anti-intuitive LR: 3x LR when Sharpe good, 0.3x when bad (Rust-only)
- #25 Trade clustering: CV(inter-trade intervals) penalty via ps[3:6]
(env_step, uses reserved portfolio state slots)
- #27 Ensemble disagreement: Q_target -= weight * ensemble_std
(mse_loss + c51_loss, buffer allocated for future ensemble wiring)
Also includes 30-task plan doc with all 28+ techniques across 3 phases.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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