initial commit

train_bot.py

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