# 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)

```bash
# 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

```bash
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`

---

## 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