31 lines
1.5 KiB
Markdown
31 lines
1.5 KiB
Markdown
# Experiment Scripts
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These scripts were used to run individual training experiments.
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Each corresponds to an entry in docs/TEST_HISTORY.md.
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| Script | Experiment | Key change |
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| mountain_v5.py | Exp 5 | v5 reward + throttle_min=0.5, direct model.learn() |
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| mountain_continue.py | Exp 4 | Continued Exp3 training |
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| mountain_high_throttle.py | Exp 3 | throttle_min=0.5, old v4 reward |
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| exp6_mountain_v5_proper.py | Exp 6 | v5 + termination, wrong steps_per_switch (=total) |
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| exp7_mountain_proper.py | Exp 7 | v5 + termination, correct steps_per_switch=6000, had phantom car issue |
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| exp8_mountain_clean.py | Exp 8 | v5 + throttle_min=0.5, single connection, correct checkpointing |
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| exp9_mountain_v5_throttle02.py | Exp 9 | v5 + throttle_min=0.2, OUR BEST MODEL |
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| exp10_two_tracks.py | Exp 10 | Two tracks via custom script (abandoned — used multitrack_runner.py instead) |
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| overnight.py | Overnight runs | mountain-only and Trial9-repeat experiments |
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| wave5_train.py | Wave 5 | generated_track only with throttle_min=0.2 |
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## Rule going forward
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ALL experiment scripts must be saved here and committed to git
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BEFORE running. Scripts in /tmp are lost on reboot.
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## Running experiments
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Use multitrack_runner.py directly for two-track training:
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python3 multitrack_runner.py --total-timesteps 90000 --steps-per-switch 6000 ...
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For single-track experiments, use the pattern from exp8/exp9:
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- VecTransposeImage(DummyVecEnv([make_env])) for env creation
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- Direct model.learn() loop with manual checkpointing
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- No close_and_switch() for single track
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