feat: 优化DQN训练配置并支持Dueling网络评估

- 将学习率调整为5e-5，批次大小增加到64，经验回放缓冲区扩大到500,000
- 启用优先经验回放，调整目标网络更新频率为1000步
- 评估时使用Dueling网络架构，训练时评估模式的ε设为0
- 更新评估结果以反映配置改进后的性能变化

强化学习个人项目报告（Atari 游戏方向）/evaluate_checkpoints.py

@@ -7,14 +7,17 @@ import torch
 import numpy as np
 import json
 
-from src.network import QNetwork
+from src.network import QNetwork, DuelingQNetwork
 from src.utils import make_env, get_device
 
 
-def evaluate_model(model_path, env, device, num_actions, num_episodes=10):
+def evaluate_model(model_path, env, device, num_actions, num_episodes=10, use_dueling=True):
     """评估单个模型"""
     state_shape = (4, 84, 84)
-    q_network = QNetwork(state_shape, num_actions).to(device)
+    if use_dueling:
+        q_network = DuelingQNetwork(state_shape, num_actions).to(device)
+    else:
+        q_network = QNetwork(state_shape, num_actions).to(device)
 
     checkpoint = torch.load(model_path, map_location=device, weights_only=False)
     q_network.load_state_dict(checkpoint['q_network'])
@@ -75,7 +78,7 @@ def main():
 
         print(f"\n评估 {model_path}...")
         avg_reward, std_reward = evaluate_model(
-            model_path, env, device, num_actions, num_episodes=10
+            model_path, env, device, num_actions, num_episodes=10, use_dueling=True
         )
 
         results.append({