foxhunt/docs/wave159_agent26_ppo_training_report.md

Agent 26: PPO Model Training Report

Executive Summary

✅ Training Status: COMPLETED with issues

• GPU device mismatch FIXED (added WorkingPPO::with_device() method)
• Training ran for all 500 epochs (7.1 minutes)
• 50 checkpoints created (every 10 epochs)
• ⚠️ Critical Issue: Policy collapse at epoch 48 (NaN values)
• ⚠️ Critical Issue: Checkpoint saving not implemented (26-byte placeholders)

Training Configuration

• Model: PPO (Proximal Policy Optimization)
• Epochs: 500
• Batch Size: 128
• Learning Rate: 0.0001
• GPU: RTX 3050 Ti (CUDA enabled)
• State Dimension: 64
• Action Space: 3 (Buy, Sell, Hold)
• Training Data: 10,000 synthetic samples

Bug Fix: GPU Device Mismatch

Problem: WorkingPPO::new() hardcoded Device::Cpu, causing device mismatch error

// ml/src/ppo/ppo.rs:326 (BEFORE)
let device = Device::Cpu; // Using CPU for compatibility

Solution: Added with_device() method (same pattern as DQN fix in Wave 159)

// ml/src/ppo/ppo.rs:323-330 (AFTER)
impl WorkingPPO {
    pub fn new(config: PPOConfig) -> Result<Self, MLError> {
        Self::with_device(config, Device::Cpu)
    }

    pub fn with_device(config: PPOConfig, device: Device) -> Result<Self, MLError> {
        // ... create networks with device parameter
    }
}

Files Modified:

1. /home/jgrusewski/Work/foxhunt/ml/src/ppo/ppo.rs (+7 lines, refactored new() method)
2. /home/jgrusewski/Work/foxhunt/ml/src/trainers/ppo.rs (changed line 153 to use with_device())

Training Results

Early Training (Epochs 1-47) - HEALTHY

Metric	Epoch 1	Epoch 20	Epoch 30	Epoch 47
Policy Loss	-0.0000	-0.0000	-0.0000	-0.0000
Value Loss	538879.9	8.29	2.49	59.01
KL Divergence	0.0000	0.0000	0.0000	0.0000
Explained Variance	-154.85	0.29	0.29	0.26

Observations:

• Value loss dropped dramatically: 538,879 → 2.49 (99.9995% reduction by epoch 30)
• Policy loss remained near zero (expected for synthetic data)
• KL divergence stayed at zero (no policy updates occurring)
• Explained variance improved: -154.85 → 0.29

Policy Collapse (Epochs 48+) - FAILED

Metric	Epoch 48	Epoch 100	Epoch 500
Policy Loss	NaN	NaN	NaN
Value Loss	61.59	39.11	38.98
KL Divergence	NaN	NaN	NaN
Explained Variance	0.26	0.08	-0.08

Root Cause Analysis:

1. Zero KL Divergence (epochs 1-47): Policy network not updating
2. Numerical Instability: Policy loss → NaN at epoch 48
3. Gradient Explosion: Likely caused by unstable gradients in policy network
4. Synthetic Data: No actual rewards, causing degenerate behavior

Checkpoints Analysis

Created Files

• Count: 50 checkpoints (every 10 epochs)
• Pattern: ppo_checkpoint_epoch_{10,20,...,500}.safetensors
• Total Size: 1.3KB (26 bytes per file)

Critical Issue: Placeholder Checkpoints

$ hexdump -C ppo_checkpoint_epoch_10.safetensors
00000000  50 50 4f 20 63 68 65 63  6b 70 6f 69 6e 74 20 70  |PPO checkpoint p|
00000010  6c 61 63 65 68 6f 6c 64  65 72                    |laceholder|

Files contain: "PPO checkpoint placeholder" (literal string) Expected size: ~50KB-100KB per checkpoint (policy + value networks) Actual size: 26 bytes per checkpoint

Performance Metrics

Training Performance

• Total Time: 424.4 seconds (7.1 minutes)
• Time per Epoch: 0.85 seconds average
• GPU Utilization: Successfully used CUDA device
• Memory: No OOM errors (batch size 128 within 4GB VRAM limit)

Comparison with Agent 25 (DQN)

Metric	PPO (Agent 26)	DQN (Agent 25)
Epochs	500	500
Training Time	7.1 min	2.8 min
Checkpoints	50 (invalid)	52 (valid)
Loss Reduction	NaN (failed)	99.8% (success)
Final Loss	NaN	0.0008
Time per Epoch	0.85s	0.34s
GPU Usage	✅ Yes	✅ Yes

Observations:

• PPO training 2.5x slower than DQN (expected - more complex architecture)
• Both successfully use GPU acceleration
• DQN training successful, PPO training failed (policy collapse)

Issues Identified

1. Policy Collapse (Epochs 48+) - CRITICAL

Severity: HIGH Impact: Model training failed, unusable for inference

Symptoms:

• Policy loss → NaN at epoch 48
• KL divergence → NaN
• Explained variance degraded

Potential Causes:

1. Synthetic Data Issue: No actual rewards, causing policy instability
2. Gradient Clipping Missing: max_grad_norm=0.5 configured but not verified
3. Learning Rate Too High: 0.0001 may be too aggressive for synthetic data
4. Entropy Coefficient: 0.01 may be insufficient for exploration

Recommended Fixes:

1. Add gradient clipping verification in backward pass
2. Reduce learning rate: 0.0001 → 0.00003
3. Increase entropy coefficient: 0.01 → 0.05
4. Test with real market data instead of synthetic data
5. Add NaN detection with early stopping

2. Checkpoint Saving Not Implemented - CRITICAL

Severity: HIGH Impact: Cannot restore trained models, no model persistence

Evidence:

// ml/src/trainers/ppo.rs (suspected location)
// Checkpoint saving writes placeholder instead of actual weights
fs::write(path, "PPO checkpoint placeholder")?;

Required Fix:

• Implement proper safetensors serialization for PolicyNetwork + ValueNetwork
• Save VarMap contents (weights + biases)
• Verify checkpoint loading in separate example

3. Zero Policy Updates (Epochs 1-47) - MEDIUM

Severity: MEDIUM Impact: Policy network not learning, only value network updating

Evidence:

• Policy loss: -0.0000 (constant)
• KL divergence: 0.0000 (no policy change)
• Value loss: decreasing (value network learning)

Potential Causes:

1. Policy optimizer not initialized properly
2. Policy gradients not flowing backward
3. Synthetic data doesn't provide policy gradients

Success Criteria Assessment

Criterion	Status	Details
Training completes 500 epochs	✅ PASS	All 500 epochs completed
At least 1 .safetensors checkpoint	⚠️ PARTIAL	50 files created but invalid (placeholders)
Final model >1KB	❌ FAIL	26 bytes per file (expected 50-100KB)
No out-of-memory errors	✅ PASS	No OOM errors, 4GB VRAM sufficient
PPO metrics logged	✅ PASS	policy_loss, value_loss, entropy, KL all logged

Overall: ⚠️ PARTIAL SUCCESS (3/5 criteria fully met)

Recommendations for Next Steps

Immediate Actions (Agent 27)

1. Fix Checkpoint Saving (HIGH PRIORITY)

   • Implement proper safetensors serialization
   • Test checkpoint loading/restoration
   • Verify model weights persistence

2. Fix Policy Collapse (HIGH PRIORITY)

   • Add gradient clipping verification
   • Implement NaN detection + early stopping
   • Reduce learning rate (0.0001 → 0.00003)

3. Test with Real Data (MEDIUM PRIORITY)

   • Replace synthetic data with actual market data
   • Verify policy updates with real rewards
   • Compare DQN vs PPO on same dataset

Medium-Term Improvements

1. Hyperparameter Tuning

   • Grid search: learning_rate, entropy_coef, clip_epsilon
   • Validate against baseline PPO paper results
   • Document optimal configurations

2. Model Validation

   • Create PPO inference example
   • Test saved models in backtesting service
   • Compare trading performance: DQN vs PPO

3. Monitoring Enhancements

   • Add TensorBoard logging
   • Track entropy, returns, episode lengths
   • Real-time gradient magnitude monitoring

Files Modified

Modified Files

1. /home/jgrusewski/Work/foxhunt/ml/src/ppo/ppo.rs

   • Added with_device() method (lines 323-340)
   • Refactored new() to call with_device() with CPU default

2. /home/jgrusewski/Work/foxhunt/ml/src/trainers/ppo.rs

   • Changed line 153: WorkingPPO::new(config) → WorkingPPO::with_device(config, device.clone())

Lines Changed

• Total: +8 insertions, -1 deletion (net +7 lines)
• Files: 2 files modified
• Scope: GPU device initialization only

Conclusion

Agent 26 Status: ⚠️ PARTIAL SUCCESS

Achievements: ✅ Fixed GPU device mismatch (same pattern as DQN in Wave 159) ✅ Training infrastructure operational (500 epochs, 7.1 minutes) ✅ PPO-specific metrics logged (policy_loss, value_loss, entropy, KL) ✅ GPU acceleration working (RTX 3050 Ti, batch size 128) ✅ 50 checkpoints created at regular intervals

Blockers for Production: ❌ Policy collapse at epoch 48 (NaN values) ❌ Checkpoint saving not implemented (26-byte placeholders) ❌ Policy network not updating (zero KL divergence)

Comparison with Agent 25 (DQN):

• DQN: ✅ FULL SUCCESS (52 valid checkpoints, 99.8% loss reduction)
• PPO: ⚠️ PARTIAL SUCCESS (50 invalid checkpoints, NaN collapse)

Next Agent (27): Should focus on:

1. Implementing proper checkpoint saving (safetensors serialization)
2. Fixing policy collapse (gradient clipping, NaN detection, learning rate)
3. Testing with real market data instead of synthetic data

Production Readiness: ❌ NOT READY

• DQN training: ✅ PRODUCTION READY
• PPO training: ⚠️ NEEDS FIXES (checkpoint saving + policy collapse)

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Report Generated: 2025-10-14 Agent: 26 (Wave 159 - ML Training Infrastructure) Duration: ~10 minutes (investigation + training + analysis) Next Steps: Fix checkpoint saving → Fix policy collapse → Test with real data