last workig state.

sac/td3_main.py

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+import os
+import numpy as np
+from stable_baselines3 import TD3
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.callbacks import EvalCallback, StopTrainingOnNoModelImprovement
+from stable_baselines3.common.env_util import make_vec_env
+import torch as th
+
+from car_racing_env import CarRacing  # <-- Pfad zu deiner Datei
+
+
+if __name__ == "__main__":
+    # Optional: reproducibility
+    np.random.seed(0)
+    th.manual_seed(0)
+
+    run_name = "td3_run_2"  # oder datetime.now().strftime("%Y%m%d_%H%M")
+
+    tensorboard_log = f"./tb_{run_name}/"
+    best_model_path = f"./{run_name}_best/"
+    eval_log_path = f"./{run_name}_eval/"
+    model_save_path = f"./{run_name}_models/"
+
+    os.makedirs(model_save_path, exist_ok=True)
+
+    train_env = Monitor(CarRacing(seed_value=0, render_mode=None))
+    model = TD3(
+        policy="MlpPolicy",
+        env=train_env,
+        verbose=1,
+        tensorboard_log=tensorboard_log,
+        learning_starts=20_000,
+    )
+
+    eval_env = Monitor(CarRacing(seed_value=1, render_mode=None))
+    stop_cb = StopTrainingOnNoModelImprovement(
+        max_no_improvement_evals=20, min_evals=5, verbose=1
+    )
+    eval_cb = EvalCallback(
+        eval_env,
+        best_model_save_path=best_model_path,
+        log_path=eval_log_path,
+        eval_freq=5_000,
+        deterministic=True,
+        render=False,
+        callback_after_eval=stop_cb,
+    )
+
+    model.learn(total_timesteps=400_000, callback=eval_cb, progress_bar=True)
+    model.save(f"{model_save_path}/td3_carracing_features")
+
+    # Kurzer Testlauf mit Rendering (optional)
+    test_env = CarRacing(seed_value=0, render_mode="human")
+    obs, info = test_env.reset()
+    done = False
+    trunc = False
+    while not (done or trunc):
+        action, _ = model.predict(obs, deterministic=True)
+        obs, reward, done, trunc, info = test_env.step(action)
+        test_env.render()
+    test_env.close()