diff --git a/DOA_SAC_sim2real.py b/DOA_SAC_sim2real.py
index b0bfa9d..4697738 100644
--- a/DOA_SAC_sim2real.py
+++ b/DOA_SAC_sim2real.py
@@ -465,7 +465,7 @@ if __name__ == "__main__":
             return True
 
 
-    tempt = 1
+    tempt = 0
     log_dir = './tensorboard/DOA_SAC_callback/'
     os.makedirs(log_dir, exist_ok=True)
     env = jakaEnv()
@@ -482,7 +482,7 @@ if __name__ == "__main__":
     else:
         obs = env.reset()
         # 改变路径为你保存模型的路径
-        model = SAC.load(r'best_model.zip', env=env)
+        model = SAC.load(r'D:\Python-Project\RL-PowerTracking-new\model\best_model.zip', env=env)
 
         for j in range(50):
             for i in range(2000):
@@ -540,7 +540,7 @@ def train_parallel(num_processes):
     # 创建共享模型（使用Stable Baselines3的SAC）
     env = jakaEnv()  # 创建环境实例
     model = SAC('MlpPolicy', env=env, verbose=1, device="cuda")  # 使用CUDA加速
-    shared_model = model.policy.to(torch.device('cuda'))  # 确保模型在GPU上
+    shared_model = model.policy.to(torch.device('cuda'))  # 显式指定模型在GPU上
     shared_model.share_memory()  # 共享模型参数
     
     # 创建进程列表
@@ -564,7 +564,7 @@ def train_process(rank, shared_model):
     env = create_arm_environment()  # 替换为实际的环境创建函数
     
     # 创建本地模型副本
-    local_model = SAC_Model().to(device)
+    local_model = SAC().to(device)
     local_model.load_state_dict(shared_model.state_dict())
     
     # 在此处替换原有训练循环为并行版本