51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import os
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import time
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from stable_baselines3 import PPO
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from stable_baselines3.common.callbacks import BaseCallback
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from flower_game_env import FlowerGameEnv
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# ---- Dein Spielbereich (anpassen!) ----
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monitor_area = {"top": 120, "left": 330, "width": 1900, "height": 1263}
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env = FlowerGameEnv(monitor_area)
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saved_model_name = "flower_bot"
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zip_file = saved_model_name + ".zip"
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class TimeBasedCheckpoint(BaseCallback):
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"""
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Speichert das Modell alle 'save_every_secs' Sekunden in 'save_prefix' + Timestamp.
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"""
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def __init__(self, save_every_secs=60, save_prefix=saved_model_name, verbose=1):
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super().__init__(verbose)
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self.save_every_secs = save_every_secs
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self.save_prefix = save_prefix
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self._last_save = time.time()
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def _on_step(self) -> bool:
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now = time.time()
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if now - self._last_save >= self.save_every_secs:
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fname = f"{self.save_prefix}"
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if self.verbose:
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print(f"[Autosave] Saving model to {fname}.zip")
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self.model.save(fname)
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self._last_save = now
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return True
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# --- Laden, falls Datei vorhanden ---
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if os.path.exists(zip_file):
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print(f"Lade existierendes Modell aus {zip_file}")
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model = PPO.load(zip_file, env=env) # Weitertrainieren mit neuem Env
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else:
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print("Starte neues Modell")
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# CNN + Dict-Observation → Verwende 'MultiInputPolicy'
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model = PPO("MultiInputPolicy", env, verbose=2)
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# Trainieren mit Autosave (jede Minute)
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model.learn(total_timesteps=500_000, callback=TimeBasedCheckpoint(100, "flower_bot"))
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# Abschluss-Speicherstand
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model.save("flower_bot_final")
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print("Training fertig. Modell gespeichert: flower_bot_final.zip")
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