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
@@ -122,12 +125,9 @@ class jakaEnv(gym.Env):
         p.resetBaseVelocity(self.blockId, linearVelocity=[0, random.choice(self.speed), 0])
         p.stepSimulation()
         # robot.servo_move_enable(True)
-        
+
         # 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)
@@ -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,
@@ -308,7 +308,7 @@ class jakaEnv(gym.Env):
         block_vel_range = np.array(self.speed)
         block_loc = np.array([0, 0, 0])
         block_rel_pos = np.array([0, 0, 0])
-        block2_pos = np.array([0.6, 0.66, 0.43]) 
+        block2_pos = np.array([0.6, 0.66, 0.43])
 
         obs = np.concatenate(
             [
@@ -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
@@ -440,32 +441,34 @@ if __name__ == "__main__":
         def _on_step(self) -> bool:
             if self.n_calls % self.check_freq == 0:
 
-              # Retrieve training reward
-              x, y = ts2xy(load_results(self.log_dir), 'timesteps')
-              if len(x) > 0:
-                  # Mean training reward over the last 100 episodes
-                  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}")
+                # Retrieve training reward
+                x, y = ts2xy(load_results(self.log_dir), 'timesteps')
+                if len(x) > 0:
+                    # Mean training reward over the last 100 episodes
+                    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}")
 
-                  # New best model, you could save the agent here
-                  if mean_reward > self.best_mean_reward:
-                      self.best_mean_reward = mean_reward
-                      # Example for saving best model
-                      if self.verbose > 0:
-                        print(f"Saving new best model to {self.save_path}")
-                      self.model.save(self.save_path)
+                    # New best model, you could save the agent here
+                    if mean_reward > self.best_mean_reward:
+                        self.best_mean_reward = mean_reward
+                        # Example for saving best model
+                        if self.verbose > 0:
+                            print(f"Saving new best model to {self.save_path}")
+                        self.model.save(self.save_path)
 
             return True
 
+
     tempt = 1
     log_dir = './tensorboard/DOA_SAC_callback/'
     os.makedirs(log_dir, exist_ok=True)
     env = jakaEnv()
     env = Monitor(env, log_dir)
     if tempt:
-        model = SAC('MlpPolicy', env=env, verbose=1, tensorboard_log=log_dir, 
+        model = SAC('MlpPolicy', env=env, verbose=1, tensorboard_log=log_dir,
                     device="cuda"
                     )
         callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
@@ -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