# DQN Hyperparameter Tuning Summary Report

**Agent**: Agent 119 - DQN Tuning Monitor  
**Date**: 2025-10-14  
**Status**: INCOMPLETE (Process terminated prematurely)

## Execution Overview

- **Planned Trials**: 50
- **Completed Trials**: 36/50 (72%)
- **Failed Trials**: 1 (trial_36 - incomplete)
- **Start Time**: 17:00 (trial_0)
- **End Time**: 18:45 (trial_35 completed, trial_36 started but not finished)
- **Total Duration**: 1 hour 45 minutes (105 minutes)
- **Average Time per Trial**: ~2.9 minutes (105 min / 36 trials)

## Trial Progress Analysis

### Completed Checkpoints
All successful trials produced checkpoint files at epoch 50:
- Size: 75,628 bytes (~74 KB) per checkpoint
- Format: SafeTensors
- Location: `/home/jgrusewski/Work/foxhunt/ml/tuning_checkpoints/trial_*/`

### Timeline
- Trials 0-9: 17:00 - 17:27 (27 minutes, ~2.7 min/trial)
- Trials 10-19: 17:30 - 17:57 (27 minutes, ~2.7 min/trial)
- Trials 20-29: 18:00 - 18:29 (29 minutes, ~2.9 min/trial)
- Trials 30-35: 18:31 - 18:45 (14 minutes, ~2.3 min/trial)
- Trial 36: Started 18:45, did not complete

## Status Assessment

### What Happened
The tuning process (PID 3907078) is no longer running. The process completed 36 trials successfully but terminated before finishing the planned 50 trials.

### Possible Causes
1. **User interruption** (Ctrl+C or kill signal)
2. **System resource constraints** (though no OOM evidence found)
3. **Time-based termination** (if a timeout was configured)
4. **Error in trial 36** (directory exists but no checkpoint)

### Available Results
- **Pilot Results**: `/home/jgrusewski/Work/foxhunt/results/tuning_pilot_dqn.json`
  - Only contains 3 trials from an earlier pilot run
  - Best trial: Trial 2 with Sharpe ratio 1.5
  - Config: learning_rate=0.001, batch_size=230, gamma=0.99, epsilon_decay=0.995
- **Full Results**: NOT AVAILABLE (no final JSON output or Optuna database found)

## Data Recovery Options

Since the process terminated without generating final results, we have several options:

### Option 1: Extract Metrics from Checkpoints
Analyze the 36 checkpoint files to extract:
- Training loss curves
- Model weights for validation
- Manual Sharpe ratio calculation through backtesting

### Option 2: Resume Tuning
Continue from trial 37 to complete the remaining 14 trials:
```bash
# Restart tuning with trials 37-50
# Would require modifying the tuning script to skip completed trials
```

### Option 3: Use Pilot Results
The pilot run shows Trial 2 performed best with:
- Learning rate: 0.001
- Batch size: 230
- Gamma: 0.99
- Epsilon decay: 0.995
- Sharpe ratio: 1.5

However, this is based on only 3 trials, not a comprehensive search.

## Recommendations

### Immediate Actions
1. **Investigate termination cause**: Check system logs, user history
2. **Validate checkpoint integrity**: Ensure all 36 checkpoints are loadable
3. **Extract available metrics**: Parse checkpoint metadata if available

### Short-term Actions
1. **Backtest checkpoint models**: Run backtests on a sample of the 36 checkpoints to estimate their Sharpe ratios
2. **Identify best performer**: Compare results to find the optimal hyperparameter combination
3. **Document findings**: Update tuning results based on manual analysis

### Long-term Actions
1. **Implement robust tuning infrastructure**:
   - Add checkpoint resumption capability
   - Store trial results incrementally (not just at completion)
   - Use Optuna's JournalStorage for fault tolerance
2. **Complete remaining trials**: If the current 36 trials show promising variance, complete the full 50-trial search
3. **Consider alternative approaches**: If trials are too homogeneous, expand the search space

## Performance Estimation

Based on the 36 completed trials:
- **Estimated total time for 50 trials**: 145 minutes (2.4 hours)
- **Time spent**: 105 minutes (72% of estimated total)
- **Time remaining**: 40 minutes for 14 trials

## Next Steps

**Priority 1 (IMMEDIATE)**:
- Determine if results can be extracted from the 36 checkpoints
- Check if any intermediate metrics were logged

**Priority 2 (SHORT-TERM)**:
- Backtest a sample of checkpoints to estimate performance
- Compare with pilot results to validate improvements

**Priority 3 (MEDIUM-TERM)**:
- Decide whether to resume tuning or work with available results
- Update tuning infrastructure to prevent data loss

## Conclusion

While the tuning run was interrupted prematurely, we have:
- **36 checkpoint files** from successful trials
- **Pilot results** showing baseline performance (Sharpe 1.5)
- **Clear performance metrics** (2.9 min/trial average)

The next agent should focus on extracting value from these 36 checkpoints through systematic backtesting and comparison.

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**Report Generated**: 2025-10-14 19:03  
**Agent**: Agent 119  
**Status**: MONITORING COMPLETE - AWAITING RESULTS EXTRACTION