# Experiment Scripts

These scripts were used to run individual training experiments.
Each corresponds to an entry in docs/TEST_HISTORY.md.

| Script | Experiment | Key change |
|---|---|---|
| mountain_v5.py | Exp 5 | v5 reward + throttle_min=0.5, direct model.learn() |
| mountain_continue.py | Exp 4 | Continued Exp3 training |
| mountain_high_throttle.py | Exp 3 | throttle_min=0.5, old v4 reward |
| exp6_mountain_v5_proper.py | Exp 6 | v5 + termination, wrong steps_per_switch (=total) |
| exp7_mountain_proper.py | Exp 7 | v5 + termination, correct steps_per_switch=6000, had phantom car issue |
| exp8_mountain_clean.py | Exp 8 | v5 + throttle_min=0.5, single connection, correct checkpointing |
| exp9_mountain_v5_throttle02.py | Exp 9 | v5 + throttle_min=0.2, OUR BEST MODEL |
| exp10_two_tracks.py | Exp 10 | Two tracks via custom script (abandoned — used multitrack_runner.py instead) |
| overnight.py | Overnight runs | mountain-only and Trial9-repeat experiments |
| wave5_train.py | Wave 5 | generated_track only with throttle_min=0.2 |

## Rule going forward
ALL experiment scripts must be saved here and committed to git
BEFORE running. Scripts in /tmp are lost on reboot.

## Running experiments
Use multitrack_runner.py directly for two-track training:
  python3 multitrack_runner.py --total-timesteps 90000 --steps-per-switch 6000 ...

For single-track experiments, use the pattern from exp8/exp9:
  - VecTransposeImage(DummyVecEnv([make_env])) for env creation
  - Direct model.learn() loop with manual checkpointing
  - No close_and_switch() for single track