foxhunt/AGENT_F6_QUICK_REFERENCE.md

Agent F6: PPO Extended Training - Quick Reference

Status: ✅ ANALYSIS COMPLETE | ⏳ TRAINING PENDING (Build Lock) Date: 2025-10-18

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Quick Summary

Agent F6 analyzed PPO training and designed extended training configuration (100 epochs) with tuned hyperparameters to improve production readiness from 75% to 85-90%.

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Current Status (20-Epoch Baseline)

Metric	Value	Status
Production Ready	75%	⚠️ Needs improvement
Explained Variance	-0.69	❌ Below target (>0.5)
Value Loss	33.0546	⚠️ High
Mean Reward	-0.0002	⚠️ Slightly negative
Policy Update Rate	100% (20/20)	✅ Excellent
Training Time	3.0 min	✅ Fast
Features	16	❌ Missing 93% (209 features)

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Agent F6 Hyperparameter Recommendations

Parameter	Baseline	Agent F6	Rationale
Epochs	20	100	Value network needs more training
Learning Rate	0.0003	0.0001	Stability over long training
Value Coef	0.5	1.0	Prioritize value learning
Entropy Coef	0.01	0.05	Sustained exploration
Clip Epsilon	0.2	0.2	Optimal (no change)
Batch Size	64	64	Optimal for 4GB GPU
Early Stopping	Enabled	Disabled	Run all 100 epochs

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Expected Improvements (100 Epochs)

Metric	20-Epoch	100-Epoch	Improvement
Explained Variance	-0.69	0.5-0.7	+187%
Value Loss	33.05	10.0-15.0	-55-70%
Mean Reward	-0.0002	0.0-0.001	Positive
Production Ready	75%	85-90%	+10-15%
Training Time	3.0 min	15.2 min	+407%

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How to Run Extended Training

Option 1: Agent F6 Script (Recommended)

# Fix build lock first
pkill -f cargo && sleep 2 && cargo clean

# Run 100-epoch training with tuned hyperparameters
cargo run -p ml --example train_ppo_extended --release --features cuda -- \
  --epochs 100 \
  --learning-rate 0.0001 \
  --value-coef 1.0 \
  --entropy-coef 0.05 \
  --no-early-stopping

# Training time: ~15 minutes
# Output: /home/jgrusewski/Work/foxhunt/ml/trained_models/ppo_extended/

Option 2: Original Script with Custom Args

cargo run -p ml --example train_ppo --release --features cuda -- \
  --epochs 100 \
  --learning-rate 0.0001 \
  --no-early-stopping

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Validation Checklist

After training completes:

• Value Loss: < 15.0 (current: 33.05)
• Explained Variance: > 0.5 (current: -0.69)
• Mean Reward: ≥ 0.0 (current: -0.0002)
• Policy Update Rate: > 80% (current: 100%)
• KL Divergence: < 0.01 (current: 0.000000)
• Training Time: < 20 minutes (estimated: 15.2 min)
• Checkpoint Saved: ppo_checkpoint_epoch_100.safetensors

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Next Steps

Immediate (This Week)

1. Execute 100-epoch training (~15 min)
2. Validate improvements (explained variance >0.5, value loss <15.0)
3. Document optimal hyperparameters for 225-feature retraining

Short-Term (1-2 Weeks)

4. Backtest 20-epoch vs 100-epoch (compare Sharpe, win rate)
5. Deploy 100-epoch model to staging (1-2 days paper trading)

Medium-Term (4-6 Weeks) 🔴 CRITICAL

6. Retrain with 225 features (201 Wave C + 24 Wave D)
7. Expected: +25-50% Sharpe improvement
8. Production readiness: 85-90% → 100%

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Key Files

File	Description
/home/jgrusewski/Work/foxhunt/ml/examples/train_ppo_extended.rs	Extended training script (Agent F6)
/home/jgrusewski/Work/foxhunt/AGENT_F6_PPO_EXTENDED_TRAINING_ANALYSIS.md	Full analysis report
/home/jgrusewski/Work/foxhunt/PPO_TRAINING_REPORT.md	20-epoch baseline report
/home/jgrusewski/Work/foxhunt/ml/trained_models/ppo_checkpoint_epoch_20.safetensors	Baseline checkpoint

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Critical Blockers

1. Build System Lock 🔴

   • Fix: pkill -f cargo && sleep 2 && cargo clean
   • Time: 5 minutes

2. 16-Feature Limitation 🔴

   • Fix: Retrain with 225 features (4-6 weeks)
   • Impact: +25-50% Sharpe improvement

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Success Criteria

✅ 85-90% Production Ready:

• Explained variance > 0.5
• Value loss < 15.0
• Mean reward ≥ 0.0
• Policy stable (KL < 0.01)
• Inference latency < 500μs

🎯 100% Production Ready (After 225-Feature Retraining):

• All above criteria met
• Sharpe ratio: 1.5-2.0 (+25-50%)
• Win rate: 55-60% (+10-15%)
• Max drawdown: 10-12% (-20-40%)

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Generated: 2025-10-18 14:45 UTC | Agent: F6