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/agent19-quick-summary.txt
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═══════════════════════════════════════════════════════════════════════════════
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AGENT 19: DQN HYPEROPT ANTI-OVERFITTING REVIEW - QUICK SUMMARY
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═══════════════════════════════════════════════════════════════════════════════
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📊 CURRENT STATE
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───────────────────────────────────────────────────────────────────────────────
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✅ Search Space: 22D (WAVE 19)
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✅ Well-tuned ranges: LR, gamma, buffer_size, entropy
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✅ Buffer size increased: [50K→100K] → [100K→500K] (WAVE 24 anti-overfitting)
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✅ Network architecture: [256, 128, 64] (3 layers, 112K params)
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🚨 CRITICAL GAPS
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───────────────────────────────────────────────────────────────────────────────
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❌ Dropout: Implemented in QNetworkConfig but NOT in hyperopt search space
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❌ Weight Decay: Missing from DQN optimizer (exists in MAMBA-2)
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⚠️ LayerNorm: Enabled by default, not tunable (low priority)
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📋 PROPOSED UPDATES
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───────────────────────────────────────────────────────────────────────────────
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ TIER 1: CRITICAL ANTI-OVERFITTING (Immediate) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ P0 🔥 ADD DROPOUT TO SEARCH SPACE │
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│ ───────────────────────────────────────────────────────────────────────────│
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│ Parameter: dropout_prob: f64 │
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│ Range: [0.1, 0.5] (linear) │
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│ Default: 0.2 (20% dropout) │
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│ Effort: LOW (QNetworkConfig.dropout_prob exists) │
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│ Impact: HIGH (+10-15% out-of-sample Sharpe) │
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│ Search: 23D (22D → 23D) │
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│ │
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│ CODE CHANGE: │
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│ ┌─────────────────────────────────────────────────────────────────────┐ │
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│ │ pub struct DQNParams { │ │
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│ │ // ... existing 22 params ... │ │
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│ │ pub dropout_prob: f64, // NEW: [0.1, 0.5] │ │
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│ │ } │ │
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│ │ │ │
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│ │ impl ParameterSpace for DQNParams { │ │
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│ │ fn continuous_bounds() -> Vec<(f64, f64)> { │ │
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│ │ vec![ │ │
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│ │ // ... existing 22 bounds ... │ │
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│ │ (0.1, 0.5), // 22: dropout_prob │ │
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│ │ ] │ │
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│ │ } │ │
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│ │ } │ │
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│ └─────────────────────────────────────────────────────────────────────┘ │
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│ │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ P1 🔧 ADD WEIGHT DECAY (AdamW Optimizer) │
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│ ───────────────────────────────────────────────────────────────────────────│
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│ Parameter: weight_decay: f64 │
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│ Range: [1e-5, 1e-3] (log scale) │
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│ Default: 1e-4 (moderate L2 penalty) │
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│ Effort: MEDIUM (upgrade Adam → AdamW) │
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│ Impact: MEDIUM (+5-10% generalization) │
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│ Search: 24D (23D → 24D) │
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│ │
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│ CODE CHANGE: │
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│ ┌─────────────────────────────────────────────────────────────────────┐ │
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│ │ pub struct DQNParams { │ │
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│ │ // ... existing params + dropout ... │ │
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│ │ pub weight_decay: f64, // NEW: [1e-5, 1e-3] log │ │
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│ │ } │ │
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│ │ │ │
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│ │ // Optimizer upgrade (ml/src/dqn/network.rs): │ │
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│ │ use candle_optimisers::AdamW; // BEFORE: Adam │ │
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│ │ let optimizer = AdamW::new( │ │
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│ │ vars.all_vars(), │ │
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│ │ learning_rate, │ │
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│ │ weight_decay // NEW PARAMETER │ │
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│ │ )?; │ │
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│ └─────────────────────────────────────────────────────────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ TIER 2: OPTIONAL REFINEMENTS (Consider if overfitting persists) │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ P2 ⚙️ LAYER NORMALIZATION TOGGLE (Low Priority) │
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│ ───────────────────────────────────────────────────────────────────────────│
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│ Parameter: use_layer_norm: bool │
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│ Current: Fixed TRUE (default enabled) │
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│ Effort: LOW (QNetworkConfig.use_layer_norm exists) │
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│ Impact: LOW (likely neutral, stability > regularization) │
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│ Verdict: SKIP unless experiments show disabling helps │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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📈 EXPECTED IMPROVEMENTS
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───────────────────────────────────────────────────────────────────────────────
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Dropout (P0): +10-15% out-of-sample Sharpe (+0.05 to +0.15)
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Weight Decay (P1): +5-10% generalization gap reduction
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Combined (P0 + P1): +15-25% total improvement
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Trial variance: -20-30% (more consistent hyperopt results)
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🔍 CURRENT PARAMETER ANALYSIS
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───────────────────────────────────────────────────────────────────────────────
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✅ WELL-TUNED (No changes needed):
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├─ learning_rate: [2e-5, 8e-5] (log) ← Narrowed from [1e-5, 3e-4]
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├─ gamma: [0.95, 0.99] (linear) ← Optimal for HFT
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├─ buffer_size: [100K, 500K] (log) ← WAVE 24: +5x diversity
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└─ entropy_coeff: [0.0, 0.1] (linear) ← Prevents policy collapse
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⚠️ ACCEPTABLE (With mitigations):
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├─ batch_size: [64, 160] (linear) ← Wave 6 enforces ≥120 for LR>2e-4
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└─ huber_delta: [10, 40] (log) ← Upper bound could amplify outliers
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❌ MISSING REGULARIZATION:
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├─ dropout_prob: NOT IN SEARCH SPACE ← Implemented but not tunable
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├─ weight_decay: NOT IN OPTIMIZER ← Exists in MAMBA-2
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└─ use_layer_norm: NOT TUNABLE ← Fixed TRUE (low priority)
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📊 OVERFITTING RISK FACTORS
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───────────────────────────────────────────────────────────────────────────────
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Network Params: 112,320 (3 layers: [256, 128, 64])
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Training Experiences: ~300,000 (replay buffer)
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Param/Data Ratio: 0.37 (HIGH OVERFITTING RISK ⚠️ )
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Solution: Dropout 0.2-0.4 + Weight Decay 1e-4 → Robust features
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📁 FILES TO MODIFY
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───────────────────────────────────────────────────────────────────────────────
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PRIMARY:
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/home/jgrusewski/Work/foxhunt/ml/src/hyperopt/adapters/dqn.rs
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├─ Add dropout_prob, weight_decay to DQNParams struct (line 160)
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├─ Update Default impl (line 260)
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├─ Update continuous_bounds() → 23D/24D (line 309)
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├─ Update from_continuous() (line 357)
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├─ Update to_continuous() (line 458)
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└─ Update param_names() (line 489)
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SECONDARY:
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/home/jgrusewski/Work/foxhunt/ml/src/trainers/dqn/config.rs
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└─ Add dropout_prob, weight_decay to DQNHyperparameters
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/home/jgrusewski/Work/foxhunt/ml/src/dqn/network.rs
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└─ Upgrade Adam → AdamW optimizer (weight_decay support)
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🧪 TESTING CHECKLIST
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───────────────────────────────────────────────────────────────────────────────
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□ cargo test --package ml --lib hyperopt::adapters::dqn::tests
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□ cargo run --bin dqn_hyperopt -- --trials 3 --epochs 5 --dry-run
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□ Verify 23D/24D search space compiles
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□ Check AdamW optimizer availability in candle-optimisers
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□ Run mini hyperopt (3 trials) to validate new params integrate correctly
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🎯 IMPLEMENTATION PRIORITY
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───────────────────────────────────────────────────────────────────────────────
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PHASE 1 (P0): Dropout Integration [EFFORT: LOW | IMPACT: HIGH]
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├─ 1. Add dropout_prob to DQNParams (→ 23D)
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├─ 2. Update ParameterSpace impl (bounds, from/to, names)
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├─ 3. Pass to QNetworkConfig in trainer
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└─ 4. Test with 3-trial dry-run
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PHASE 2 (P1): Weight Decay Integration [EFFORT: MED | IMPACT: MED]
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├─ 1. Add weight_decay to DQNParams (→ 24D)
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├─ 2. Upgrade Adam → AdamW optimizer
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├─ 3. Verify candle-optimisers supports AdamW
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└─ 4. Test gradient updates with weight decay
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PHASE 3 (P2): Optional LayerNorm Toggle [EFFORT: LOW | IMPACT: LOW]
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└─ Only implement if P0+P1 insufficient (unlikely)
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📊 RATIONALE
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───────────────────────────────────────────────────────────────────────────────
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WHY DROPOUT IS CRITICAL:
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├─ Network capacity: 112K params vs 300K experiences (0.37 ratio)
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├─ Current dropout: Likely 0.0 (no regularization) or fixed 0.1
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├─ Risk: Memorizing training distribution → poor generalization
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└─ Fix: Dropout 0.2-0.4 forces robust feature learning
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WHY WEIGHT DECAY HELPS:
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├─ Current: Adam without L2 penalty → unbounded parameter growth
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├─ Risk: Large weights → high noise sensitivity → overfitting
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├─ Evidence: MAMBA-2 uses weight_decay=1e-3 for stability
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└─ Fix: Weight decay → bounded parameters → smoother decisions
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WHY LAYER NORMALIZATION IS LOWER PRIORITY:
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├─ Current: Already enabled by default (use_layer_norm=true)
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├─ Purpose: Gradient stability (not primarily anti-overfitting)
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├─ Tradeoff: Adds parameters (2× per layer) → could increase overfitting
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└─ Verdict: Keep enabled (stability > cost), don't tune unless needed
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═══════════════════════════════════════════════════════════════════════════════
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STATUS: ✅ REVIEW COMPLETE - READY FOR CODER AGENT IMPLEMENTATION
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═══════════════════════════════════════════════════════════════════════════════
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Next: Hand off to Coder Agent for Dropout (P0) and Weight Decay (P1) integration
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Report: /home/jgrusewski/Work/foxhunt/docs/codebase-cleanup/agent19-dqn-hyperopt-anti-overfitting-review.md
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