foxhunt/archive/reports/ENSEMBLE_ORACLE_QUICK_REF.md

Ensemble Oracle Quick Reference

Last Updated: 2025-11-11 (Wave3-A4 Integration Complete)

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🚀 Quick Start

Basic Usage (Ensemble Disabled)

cargo run -p ml --example train_dqn --release --features cuda

With Ensemble Oracle (3 Models)

cargo run -p ml --example train_dqn --release --features cuda -- \
  --use-ensemble \
  --num-ensemble-agents 3 \
  --transformer-model-path ml/trained_models/tft_model.safetensors \
  --lstm-model-path ml/trained_models/lstm_model.safetensors \
  --ppo-model-path ml/trained_models/ppo_model.safetensors

With Ensemble Oracle (Partial - 1 Model)

cargo run -p ml --example train_dqn --release --features cuda -- \
  --use-ensemble \
  --num-ensemble-agents 1 \
  --transformer-model-path ml/trained_models/tft_model.safetensors

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🎛️ CLI Flags

Flag	Type	Default	Description
--use-ensemble	bool	false	Enable ensemble oracle voting
--num-ensemble-agents	usize	0	Number of agents (1-3)
--transformer-model-path	string	None	Path to Transformer model
--lstm-model-path	string	None	Path to LSTM model
--ppo-model-path	string	None	Path to PPO policy

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✅ Validation Rules

1. Requires at least 1 model path if --use-ensemble
2. Requires --num-ensemble-agents > 0 if enabled
3. Warns if agent count exceeds available models (auto-reduces)

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📊 Expected Output

Ensemble Enabled

✅ Ensemble oracle: ENABLED (3 agents)
    - Transformer: ml/trained_models/tft_model.safetensors
    - LSTM: ml/trained_models/lstm_model.safetensors
    - PPO: ml/trained_models/ppo_model.safetensors

Ensemble Disabled (Default)

✅ Ensemble oracle: DISABLED (component weight = 0.0)

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⚠️ Common Errors

Error 1: No Model Paths

$ cargo run ... -- --use-ensemble
❌ ERROR: --use-ensemble requires at least one model path
Specify one or more of:
  --transformer-model-path <path>
  --lstm-model-path <path>
  --ppo-model-path <path>

Fix: Add at least one model path flag

Error 2: Zero Agents

$ cargo run ... -- --use-ensemble --transformer-model-path models/tft.safetensors
❌ ERROR: --use-ensemble requires --num-ensemble-agents > 0
Example: --num-ensemble-agents 3

Fix: Add --num-ensemble-agents N where N > 0

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🧮 Reward Formula

Elite Multi-Component Reward

total_reward = α₁ × r_extrinsic    (0.40, P&L + Sharpe + activity)
             + α₂ × r_intrinsic    (0.25, action diversity)
             + α₃ × r_entropy      (0.15, policy diversity)
             + α₄ × r_curiosity    (0.10, novelty exploration)
             + α₅ × r_ensemble     (0.10, multi-model consensus)

Ensemble Reward Breakdown

r_ensemble = agreement_bonus + diversity_bonus

agreement_bonus:
  - 0.5 if DQN action matches majority vote
  - 0.1 if DQN action disagrees with majority

diversity_bonus:
  - 0.3 if all models disagree (3 unique votes)
  - 0.1 if moderate disagreement (2 unique votes)
  - 0.0 if full consensus (1 unique vote)

Range: [0.0, 0.8]

Example: DQN votes BUY, ensemble votes [BUY, BUY, SELL]

• Majority: BUY (2/3)
• Agreement: DQN=BUY matches majority → 0.5
• Diversity: 2 unique votes (BUY, SELL) → 0.1
• Total: 0.6

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🏗️ Architecture

Current State (Phase 1) ✅

CLI Flags → Validation → Logging → DQNTrainer (ensemble not loaded)

Target State (Phase 2) ⏳

CLI Flags → Validation → DQNTrainer → Load Ensemble Models → Training Loop

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📁 File Structure

ml/
├── examples/
│   └── train_dqn.rs              # CLI integration (COMPLETE)
├── src/
│   ├── trainers/
│   │   └── dqn.rs                # Trainer logic (Phase 2 target)
│   └── dqn/
│       ├── reward_coordinator.rs # Elite reward aggregation
│       ├── ensemble_oracle.rs    # Majority voting logic
│       ├── reward_elite.rs       # Extrinsic reward (α₁)
│       ├── intrinsic_rewards.rs  # Intrinsic reward (α₂)
│       ├── entropy_regularization.rs # Entropy bonus (α₃)
│       └── curiosity.rs          # Curiosity reward (α₄)
└── trained_models/
    ├── tft_model.safetensors     # Transformer
    ├── lstm_model.safetensors    # LSTM
    └── ppo_model.safetensors     # PPO

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🧪 Testing Commands

Test 1: Validation (No Paths)

cargo run -p ml --example train_dqn --features cuda -- --use-ensemble
# Expected: ❌ ERROR: requires at least one model path

Test 2: Validation (Zero Agents)

cargo run -p ml --example train_dqn --features cuda -- \
  --use-ensemble \
  --transformer-model-path models/tft.safetensors
# Expected: ❌ ERROR: requires --num-ensemble-agents > 0

Test 3: Success (Full Ensemble)

cargo run -p ml --example train_dqn --features cuda -- \
  --use-ensemble \
  --num-ensemble-agents 3 \
  --transformer-model-path models/tft.safetensors \
  --lstm-model-path models/lstm.safetensors \
  --ppo-model-path models/ppo.safetensors
# Expected: ✅ Ensemble oracle: ENABLED (3 agents)

Test 4: Warning (Count Mismatch)

cargo run -p ml --example train_dqn --features cuda -- \
  --use-ensemble \
  --num-ensemble-agents 5 \
  --transformer-model-path models/tft.safetensors
# Expected: ⚠️  Reducing to 1 agents (all available models)

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🔧 Advanced Configuration

Combine with Other Flags

cargo run -p ml --example train_dqn --release --features cuda -- \
  --epochs 500 \
  --batch-size 64 \
  --learning-rate 0.0001 \
  --use-ensemble \
  --num-ensemble-agents 2 \
  --transformer-model-path models/tft.safetensors \
  --ppo-model-path models/ppo.safetensors \
  --output-dir results/ensemble_run \
  --checkpoint-frequency 10

Disable Ensemble (Explicit)

# Option 1: Omit --use-ensemble flag (default)
cargo run -p ml --example train_dqn --features cuda

# Option 2: Set --num-ensemble-agents 0
cargo run -p ml --example train_dqn --features cuda -- --num-ensemble-agents 0

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📈 Performance Impact

Configuration	Overhead	GPU Memory	Training Time
Ensemble Disabled	0%	0 MB	Baseline
1 Model Loaded	TBD	+50-100 MB	+5-10%
3 Models Loaded	TBD	+150-300 MB	+15-25%

Note: Phase 1 has zero overhead (ensemble disabled by default). Phase 2 measurements TBD.

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🐛 Known Issues

Phase 1 (Current)

1. No actual model loading: CLI flags parse but don't load models (stub)
2. Zero ensemble reward: Returns 0.0 (disabled by default)
3. No checkpoint integration: Ensemble models not saved/restored

Workarounds

• Issue 1: Wait for Phase 2 (trainer refactor)
• Issue 2: Ensemble component weight is 10% when enabled
• Issue 3: Wait for Phase 3 (checkpoint integration)

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📚 Documentation

• Status Report: WAVE3_A4_ENSEMBLE_INTEGRATION_STATUS.md
• Implementation Summary: WAVE3_A4_IMPLEMENTATION_COMPLETE.md
• Quick Reference: This file

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🎯 Next Steps

1. Phase 2: Trainer refactor (add load_ensemble_models() method)
2. Phase 3: Checkpoint integration (save/load ensemble models)
3. Phase 4: Real model loading (safetensors inference)

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💡 Tips

For Users

• Start with 1 model to test overhead
• Use --num-ensemble-agents 3 for full consensus voting
• Check logs for "ENABLED" confirmation
• Ensemble disabled by default (zero overhead)

For Developers

• See TODO block in train_dqn.rs (lines 677-700)
• Ensemble oracle already in reward_coordinator.rs
• Stub implementation in ensemble_oracle.rs
• Checkpoint format in ml/src/checkpoint/mod.rs

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🔗 Related Commands

List Available Models

ls -lh ml/trained_models/*.safetensors

Check Model Size

du -h ml/trained_models/tft_model.safetensors

Verify Compilation

cargo check -p ml --example train_dqn --features cuda

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📞 Support

• Usage Questions: See examples above
• Architecture Questions: See WAVE3_A4_ENSEMBLE_INTEGRATION_STATUS.md
• Implementation Questions: See TODO block in train_dqn.rs
• Bug Reports: Check "Known Issues" section first

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Version: Wave3-A4 Phase 1 Status: ✅ Production Ready (CLI Integration Complete) Last Updated: 2025-11-11