feat(ml): WAVE 29 DQN Codebase Cleanup & Refactoring Campaign
BREAKING CHANGES: - Removed orphaned dqn.rs monolithic trainer (4,975 lines) - Removed orphaned dqn_ensemble.rs module (816 lines) - Removed orphaned tft.rs and tft_complete_int8_integration_test.rs - TFT trainer split into modular directory structure DQN Module Refactoring: - Split trainers/dqn.rs into modular structure (config.rs, statistics.rs, trainer.rs) - Fixed hyperopt 39D search space (continuous params only) - Boolean flags (use_dueling, use_double_dqn, use_per, use_noisy_nets) are now FIXED architectural decisions - use_distributional defaults to false (Candle BUG #36 - scatter_add gradient issues) Clean Module Structure: - ml/src/trainers/dqn/ directory with proper mod.rs exports - ml/src/trainers/tft/ directory with config.rs, types.rs, model.rs, trainer.rs, tests.rs - All P0 features validated: TD-error clamping, batch diversity, LR scheduler, priority staleness Documentation: - Added comprehensive docs in docs/codebase-cleanup/ - ADR-001 for DQN refactoring decisions - Rainbow DQN component matrix and quick reference guides Build Status: Compiles with zero errors 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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jgrusewski
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docs/codebase-cleanup/spectral_norm_visual_summary.txt
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docs/codebase-cleanup/spectral_norm_visual_summary.txt
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╔══════════════════════════════════════════════════════════════════════════════╗
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║ SPECTRAL NORMALIZATION RESEARCH SUMMARY ║
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║ Agent 15 - Complete ║
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╚══════════════════════════════════════════════════════════════════════════════╝
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┌────────────────────────────────────────────────────────────────────────────┐
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│ 🎯 OBJECTIVE: Research spectral normalization for DQN anti-overfitting │
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└────────────────────────────────────────────────────────────────────────────┘
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┌────────────────────────────────────────────────────────────────────────────┐
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│ ✅ FEASIBILITY VERDICT: YES - CAN BE IMPLEMENTED │
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│ Complexity: MEDIUM | Time: 5-8 days | Performance: -5-10% │
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└────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────── KEY FINDINGS ───────────────────────────────────┐
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│ │
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│ 1. CANDLE SUPPORT │
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│ ❌ No built-in spectral normalization in candle-nn v0.9.1 │
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│ ✅ CAN implement using power iteration algorithm │
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│ ✅ Existing power iteration examples in codebase to adapt │
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│ │
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│ 2. IMPLEMENTATION APPROACH │
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│ Algorithm: Power Iteration (Miyato et al., 2018) │
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│ ┌────────────────────────────────────────────────────┐ │
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│ │ for i in 0..n_iters: │ │
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│ │ v = W^T * u / ||W^T * u|| # Right singular vec │ │
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│ │ u = W * v / ||W * v|| # Left singular vec │ │
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│ │ σ = u^T * W * v # Spectral norm │ │
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│ │ W_norm = W / σ # Normalized weights │ │
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│ └────────────────────────────────────────────────────┘ │
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│ │
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│ 3. INTEGRATION POINT │
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│ Wrap existing NoisyLinear in rainbow_network.rs │
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│ ┌────────────────────────────────────────────────────┐ │
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│ │ pub struct SpectralNoisyLinear { │ │
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│ │ noisy_linear: NoisyLinear, │ │
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│ │ spectral_norm: Option<SpectralNorm>, │ │
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│ │ } │ │
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│ └────────────────────────────────────────────────────┘ │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌──────────────────────── COST-BENEFIT ANALYSIS ─────────────────────────────┐
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│ │
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│ 💰 COSTS │ 📈 BENEFITS │
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│ ───────────────────────────────── │ ──────────────────────────────────── │
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│ • Dev time: 5-8 days │ • Overfitting reduction: 10-20% │
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│ • Performance: -5-10% speed │ • Better generalization │
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│ • Complexity: Custom impl needed │ • Training stability │
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│ • Memory: +0.1% params (minimal) │ • State-of-the-art technique │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌──────────────────────── IMPLEMENTATION ROADMAP ────────────────────────────┐
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│ │
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│ 📅 PHASE 1: Core Implementation (2-3 days) │
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│ ├─ Create SpectralNorm struct │
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│ ├─ Implement power iteration │
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│ ├─ Write unit tests │
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│ └─ Benchmark performance │
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│ │
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│ 📅 PHASE 2: DQN Integration (1-2 days) │
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│ ├─ Add use_spectral_norm config flag │
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│ ├─ Wrap NoisyLinear layers │
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│ ├─ Update initialization │
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│ └─ Integration tests │
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│ │
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│ 📅 PHASE 3: Validation (2-3 days) │
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│ ├─ Hyperopt with/without spectral norm │
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│ ├─ Compare validation metrics │
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│ ├─ Measure overfitting reduction │
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│ └─ Ablation studies │
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│ │
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│ ⏱️ TOTAL: 5-8 days │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────── RECOMMENDATION MATRIX ──────────────────────────────┐
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│ │
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│ 🟢 IMPLEMENT IF: │ 🔴 DON'T IMPLEMENT IF: │
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│ ───────────────────────────────── │ ──────────────────────────────────── │
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│ • Validation >> Training loss │ • Current regularization works │
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│ • Overfitting is critical issue │ • Limited dev resources │
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│ • Need SOTA anti-overfit tech │ • Performance overhead unacceptable │
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│ • Can afford 5-8 days + 5-10% │ • Simple alternatives untried │
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│ │
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│ 🟡 SIMPLE ALTERNATIVES (try first): │
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│ ──────────────────────────────────────────────────────────────────────── │
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│ • Increase dropout: 0.3 → 0.5 (1 line change) │
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│ • Add weight decay to optimizer (already supported) │
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│ • Implement early stopping (simple) │
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│ • Reduce network capacity (config change) │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌───────────────────────── TECHNICAL DETAILS ────────────────────────────────┐
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│ │
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│ 📊 Performance Impact │
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│ • Training time: +5-10% slower │
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│ • Memory: +0.1% parameters (2 * hidden_dim per layer) │
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│ • Power iterations: 1-3 typically sufficient │
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│ • Convergence: Fast (track ||σ_new - σ_old||) │
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│ │
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│ 🔧 Implementation Complexity │
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│ • Lines of code: ~300 LOC │
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│ • Dependencies: None (pure candle tensors) │
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│ • Integration: Wrap existing NoisyLinear │
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│ • Testing: Unit + integration + validation │
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│ │
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│ 📚 References │
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│ • Miyato et al. (2018) - Spectral Normalization for GANs │
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│ • Gouk et al. (2021) - Lipschitz Regularization │
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│ • Yoshida & Miyato (2017) - Spectral Norm for DRL │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌───────────────────── EXISTING CODE PATTERNS ───────────────────────────────┐
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│ │
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│ Found power iteration examples in codebase: │
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│ ✅ ml/src/regime/transition_matrix.rs:273 │
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│ └─ Power iteration: π^(k+1) = π^(k) * P │
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│ │
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│ ✅ ml/src/checkpoint/enterprise_implementations.rs:308 │
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│ └─ Simple power iteration for largest eigenvalue │
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│ │
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│ Can adapt these patterns for spectral norm calculation! │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌───────────────────────── DECISION CHECKLIST ───────────────────────────────┐
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│ │
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│ Before implementing, confirm: │
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│ │
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│ [ ] Overfitting measurably severe (validation >> training) │
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│ [ ] Team approved 5-8 days development time │
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│ [ ] Performance overhead (5-10%) acceptable │
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│ [ ] Current regularization proven insufficient │
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│ [ ] Alternative simple solutions already tried │
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│ │
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│ IF ALL CHECKED → 🟢 IMPLEMENT │
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│ IF ANY UNCHECKED → 🟡 CONSIDER ALTERNATIVES │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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┌──────────────────────────── DELIVERABLES ──────────────────────────────────┐
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│ │
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│ 📄 Research Documents Created: │
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│ 1. spectral_normalization_research.md (full report) │
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│ 2. spectral_norm_quick_ref.md (decision guide) │
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│ 3. spectral_norm_visual_summary.txt (this file) │
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│ │
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│ 🎯 Next Steps: │
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│ → Team decision on implementation priority │
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│ → If approved: Begin Phase 1 (core implementation) │
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│ → If not: Try simple alternatives first │
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│ │
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└──────────────────────────────────────────────────────────────────────────────┘
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╔══════════════════════════════════════════════════════════════════════════════╗
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║ STATUS: ✅ RESEARCH COMPLETE ║
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║ Agent 15 - Spectral Normalization ║
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║ Awaiting Team Decision ║
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╚══════════════════════════════════════════════════════════════════════════════╝
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