新增 DoubleDQN 训练与评估脚本与配置文件，且更改了目录

src/Car-Racing/configs/double_dqn.yaml

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+# Extends base DQN config
+_extends: ./dqn.yaml
+
+hyperparameters:
+  # Double DQN specific
+  tau: 0.005                    # For soft target updates
+  update_target_every: 10000     # Less frequent hard updates

src/Car-Racing/doubledqn_model/eval_doubledqn.py

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+import os
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import double_dqn_model.double_dqn as DDQN
+import gymnasium as gym
+from gymnasium.wrappers import GrayscaleObservation, ResizeObservation, FrameStackObservation, RecordVideo
+
+# Load the saved model
+save_dir = 'training\saved_models'
+model_file = 'DoubleDQN.pt'
+driver = DDQN.DoubleDQNAgent(
+    state_space_shape=(4, 84, 84),
+    action_n=5,
+    load_state=True,
+    load_model=model_file
+)
+
+# Evaluate
+def evaluate_agent(agent, num_episodes=5, render=True):
+    env = gym.make("CarRacing-v3", continuous=False, render_mode="rgb_array")
+    env = RecordVideo(env, video_folder='videos\DoubleDQN')
+    env = DDQN.SkipFrame(env, skip=4)
+    env = GrayscaleObservation(env)
+    env = ResizeObservation(env, (84, 84))
+    env = FrameStackObservation(env, stack_size=4)
+    agent.epsilon = 0
+    seeds_list = [i for i in range(num_episodes)]
+    scores = []
+    for episode, seed in enumerate(seeds_list):
+        state, info = env.reset(seed=seed)
+        score = 0
+        updating = True
+        while updating:
+            action = agent.take_action(state)
+            state, reward, terminated, truncated, info = env.step(action)
+            score += reward
+            updating = not (terminated or truncated)
+        scores.append(score)
+        print(f"Evaluation Episode {episode+1}/{num_episodes} | Seed: {seed} | Score: {score:.1f}")
+    env.close()
+    return sum(scores) / len(scores)
+
+avg_score = evaluate_agent(driver, num_episodes=5, render=True)
+print(f"Average evaluation score: {avg_score:.1f}")