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| """ | |
| The whole training pipeline (about 30-60 minutes on a laptop): | |
| python scripts/train.py data, models, traffic-light tuning, reinforcement learning | |
| python scripts/train.py --skip-data reuse data/training/*.csv | |
| python scripts/train.py --skip-tune skip the max-pressure tuning | |
| python scripts/train.py --skip-rl skip reinforcement learning | |
| python scripts/train.py --only-rl reinforcement learning only (rounds: --rl-rounds 3) | |
| """ | |
| import argparse | |
| import json | |
| import _path # noqa: F401 | |
| from traffix.training import datagen, rl, train_models, tune | |
| if __name__ == "__main__": | |
| p = argparse.ArgumentParser() | |
| p.add_argument("--skip-data", action="store_true") | |
| p.add_argument("--skip-tune", action="store_true") | |
| p.add_argument("--trials", type=int, default=8) | |
| p.add_argument("--skip-rl", action="store_true") | |
| p.add_argument("--only-rl", action="store_true") | |
| p.add_argument("--only-predictive", action="store_true") | |
| p.add_argument("--rl-rounds", type=int, default=3) | |
| a = p.parse_args() | |
| if a.only_predictive: | |
| print("Tuning the predictive traffic light agent...") | |
| print(tune.tune_predictive(a.trials)) | |
| raise SystemExit | |
| if a.only_rl: | |
| print("Reinforcement learning (Fitted Q-Iteration)...") | |
| print(rl.train(a.rl_rounds)) | |
| raise SystemExit | |
| if not a.skip_data: | |
| print("1/4 Generating training data (2025 dates)...") | |
| print(datagen.generate()) | |
| print("2/4 Training models...") | |
| print(json.dumps(train_models.train_all(), indent=1)) | |
| if not a.skip_tune: | |
| print("3/4 Tuning traffic light, corridor and predictive-agent parameters...") | |
| print(tune.tune(a.trials)) | |
| print(tune.tune_predictive(a.trials)) | |
| if not a.skip_rl: | |
| print("4/4 Reinforcement learning for the traffic light agent...") | |
| print(rl.train(a.rl_rounds)) | |
| print("Models saved in models/") | |