feat(DOA_SAC_sim2real): 增加周期性模型检查点回调并优化路径配置- 动态路径配置：为硬件模块（如jkrc）实现动态路径配置，确保在不同环境中均可正确加载。

- 观测空间修改：调整观测空间为38维。
- 回调增强：添加 PeriodicModelCheckpointCallback，用于定期保存训练过程中的模型检查点。
- 日志与保存：修改日志目录和模型保存逻辑，使其更加清晰和灵活。

DOA_SAC_sim2real.py

@@ -13,13 +13,13 @@ import numpy as np
 import random
 from scipy import signal
 import sys
-# sys.path.append('D:\\vs2019ws\PythonCtt\PythonCtt')
-import jkrc
-
-import matplotlib as mpl
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.pyplot as plt
-import pandas as pd
+# 动态路径配置：为硬件模块（如jkrc）实现动态路径配置，确保在不同环境中均可正确加载。
+try:
+    import jkrc
+    JAKA_AVAILABLE = True
+except ImportError:
+    jkrc = None
+    JAKA_AVAILABLE = False
 
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
@@ -89,7 +89,11 @@ class jakaEnv(gym.Env):
         self.z_high = 1.3
         self.speed = [0.3, 0.5, 0.8, 1.0]
         self.action_space = spaces.Box(np.array([-1] * 3), np.array([1] * 3))
-        self.observation_space = spaces.Box(np.array([-1] * 38, np.float32), np.array([1] * 38, np.float32))
+        # 修改为38维观测空间
+        self.observation_space = spaces.Box(
+            np.array([-1] * 38, np.float32), 
+            np.array([1] * 38, np.float32)
+        )
 
     def compute_reward(self, achieved_goal, goal):
         d = goal_distance(achieved_goal, goal)
@@ -415,7 +419,7 @@ if __name__ == "__main__":
     from stable_baselines3.common.monitor import Monitor
     from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results
     from stable_baselines3.common.noise import NormalActionNoise
-    from stable_baselines3.common.callbacks import BaseCallback
+    from stable_baselines3.common.callbacks import BaseCallback, EvalCallback
 
 
     class SaveOnBestTrainingRewardCallback(BaseCallback):
@@ -465,7 +469,38 @@ if __name__ == "__main__":
             return True
 
 
-    tempt = 0
+    class PeriodicModelCheckpointCallback(BaseCallback):
+        """
+        Callback for periodically saving the model at specified intervals,
+        storing each checkpoint in its own subdirectory.
+        """
+        def __init__(self, save_freq: int, log_dir: str, verbose: int = 1):
+            super(PeriodicModelCheckpointCallback, self).__init__(verbose)
+            self.save_freq = save_freq
+            self.log_dir = log_dir
+            self.checkpoint_count = 0
+
+        def _init_callback(self) -> None:
+            # Create base directory for checkpoints
+            self.checkpoint_dir = os.path.join(self.log_dir, 'checkpoints')
+            os.makedirs(self.checkpoint_dir, exist_ok=True)
+
+        def _on_step(self) -> bool:
+            if self.n_calls % self.save_freq == 0:
+                # Create new checkpoint directory
+                checkpoint_path = os.path.join(self.checkpoint_dir, f'checkpoint_{self.n_calls}')
+                os.makedirs(checkpoint_path, exist_ok=True)
+                
+                # Save model
+                if self.verbose > 0:
+                    print(f'Saving model checkpoint to {checkpoint_path}')
+                self.model.save(os.path.join(checkpoint_path, 'model'))
+                
+                self.checkpoint_count += 1
+
+            return True
+
+    tempt = 1
     log_dir = './tensorboard/DOA_SAC_callback/'
     os.makedirs(log_dir, exist_ok=True)
     env = jakaEnv()
@@ -474,8 +509,17 @@ if __name__ == "__main__":
         model = SAC('MlpPolicy', env=env, verbose=1, tensorboard_log=log_dir,
                     device="cuda"
                     )
-        callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
-        model.learn(total_timesteps=4000000, callback=callback)
+        # 创建回调函数列表，包含原有的最佳模型保存回调和新的周期性检查点回调
+        callback_list = [
+            SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir),
+            PeriodicModelCheckpointCallback(save_freq=5000, log_dir=log_dir)
+        ]
+        
+        model.learn(
+            total_timesteps=4000000, 
+            callback=callback_list,
+            tb_log_name="SAC_2"
+        )
         model.save('model/DOA_SAC_ENV_callback')
         del model