# DQN Hyperparameter Tuning - Quick Reference

## Status Check (One-Liner)

```bash
ps aux | grep tune_hyperparameters | grep -v grep && tail -5 /tmp/tuning_run.log
```

## Monitor Progress

```bash
# Real-time logs
tail -f /tmp/tuning_run.log

# Dashboard (refresh every 30s)
watch -n 30 /tmp/monitor_tuning.sh

# Check if still running
pgrep -f "tune_hyperparameters.*50" || echo "Process finished!"
```

## Stop/Kill

```bash
# Graceful stop
kill $(pgrep -f "tune_hyperparameters.*50")

# Force kill
kill -9 $(pgrep -f "tune_hyperparameters.*50")
```

## View Results

```bash
# Best hyperparameters
cat results/dqn_tuning_50trials.json | jq '.best_trial'

# Summary statistics
cat results/dqn_tuning_50trials.json | jq '{
  total_trials,
  successful_trials,
  failed_trials,
  best_sharpe: .best_trial.sharpe_ratio,
  best_loss: .best_trial.final_loss
}'

# Top 5 trials by Sharpe ratio
cat results/dqn_tuning_50trials.json | jq '.all_results | sort_by(-.sharpe_ratio) | .[0:5]'
```

## Re-run if Needed

```bash
# Same 50-trial study
target/release/examples/tune_hyperparameters \
  --num-trials 50 \
  --epochs-per-trial 50 \
  --data-dir test_data/real/databento/ml_training \
  --output results/dqn_tuning_50trials_v2.json \
  > /tmp/tuning_run_v2.log 2>&1 &

# Quick 10-trial study
target/release/examples/tune_hyperparameters \
  --num-trials 10 \
  --epochs-per-trial 30 \
  --data-dir test_data/real/databento/ml_training \
  --output results/dqn_tuning_quick.json
```

## Process Info

| Item | Value |
|------|-------|
| PID | 3911478 |
| Status | Running Trial 0/50 |
| Progress | Epoch 46/50 (92% of first trial) |
| ETA | ~4 hours (21:00 CEST) |
| Output | results/dqn_tuning_50trials.json |

## Expected Best Config (Pilot Study)

Based on pilot study (Agent 49):
```yaml
Learning Rate: 0.001
Batch Size: 230
Gamma: 0.99
Epsilon Decay: 0.999
```

This study will validate and potentially improve upon this baseline.