refactor(env): 优化 DOA_SAC_sim2real 脚本

- 修改了 URDF 文件路径
- 删除了冗余代码和注释
- 调整了代码格式和缩进，提高了可读性
- 更新了模型保存路径

DOA_SAC_sim2real.py

@@ -17,13 +17,16 @@ import matplotlib as mpl
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 import pandas as pd
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 MAX_EPISODE_LEN = 20 * 100
 x = []
 y = []
 z = []
-#运动模式
+
+
+# 运动模式
 # PI=3.1415926
 # ABS = 0 # 绝对运动
 # INCR = 1 # 增量运动
@@ -126,9 +129,6 @@ class jakaEnv(gym.Env):
         # robot.servo_p(cartesian_pose=[dx*100, dy*100, dz*50, 0, 0, 0], move_mode=1)
         # time.sleep(0.008)
 
-
-
-
         state_object, state_object_orienation = p.getBasePositionAndOrientation(self.objectId)
         twist_object, twist_object_orienation = p.getBaseVelocity(self.objectId)
         state_robot = p.getLinkState(self.jaka_id, 6)[0]
@@ -282,7 +282,7 @@ class jakaEnv(gym.Env):
         reset_realposes = [0.8, 2.4, 1.3, 1, -1.57, 0]
         # 改变路径为你机械臂的URDF文件路径
         self.jaka_id = p.loadURDF(
-            r"D:\Python\robot_DRL\env\lib64\urdf\jaka_description\urdf\jaka_description.urdf",
+            "urdf/jaka_description/urdf/jaka_description.urdf",
             basePosition=[0, 0.5, 0.65],
             baseOrientation=p.getQuaternionFromEuler([0, 0, 3.14]),
             useFixedBase=True,
@@ -425,6 +425,7 @@ if __name__ == "__main__":
           It must contains the file created by the ``Monitor`` wrapper.
         :param verbose: Verbosity level.
         """
+
         def __init__(self, check_freq: int, log_dir: str, verbose: int = 1):
             super(SaveOnBestTrainingRewardCallback, self).__init__(verbose)
             self.check_freq = check_freq
@@ -447,7 +448,8 @@ if __name__ == "__main__":
                     mean_reward = np.mean(y[-100:])
                     if self.verbose > 0:
                         print(f"Num timesteps: {self.num_timesteps}")
-                    print(f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")
+                        print(
+                            f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")
 
                     # New best model, you could save the agent here
                     if mean_reward > self.best_mean_reward:
@@ -459,6 +461,7 @@ if __name__ == "__main__":
 
             return True
 
+
     tempt = 1
     log_dir = './tensorboard/DOA_SAC_callback/'
     os.makedirs(log_dir, exist_ok=True)
@@ -476,8 +479,7 @@ if __name__ == "__main__":
     else:
         obs = env.reset()
         # 改变路径为你保存模型的路径
-        model = SAC.load(r'C:\Users\fly\PycharmProjects\RL-PowerTracking-new\best_model.zip', env=env)
-
+        model = SAC.load(r'best_model.zip', env=env)
 
         for j in range(50):
             for i in range(2000):
@@ -498,7 +500,6 @@ if __name__ == "__main__":
                     break
             break
 
-
     # 三维
     # fig1 = plt.figure("机械臂运行轨迹")
     # ax = fig1.add_subplot(projection="3d")  # 三维图形