C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\callbacks.py:414: UserWarning: Training and eval env are not of the same type<stable_baselines3.common.vec_env.subproc_vec_env.SubprocVecEnv object at 0x0000026FC1285150> != <stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv object at 0x0000026FC7ABB850>
warnings.warn("Training and eval env are not of the same type" f"{self.training_env} != {self.eval_env}")
Traceback (most recent call last):
File "D:\File\Dissertation\深度学习\FR5_Reinforcement-learning-master\Fr5_train.py", line 171, in
model.learn(total_timesteps=TIMESTEPS,
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\ppo\ppo.py", line 315, in learn
return super().learn(
^^^^^^^^^^^^^^
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 300, in learn
continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 224, in collect_rollouts
rollout_buffer.add(
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\buffers.py", line 470, in add
self.observations[self.pos] = np.array(obs)
ValueError: could not broadcast input array from shape (16,12) into shape (16,1,12)
C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\callbacks.py:414: UserWarning: Training and eval env are not of the same type<stable_baselines3.common.vec_env.subproc_vec_env.SubprocVecEnv object at 0x0000026FC1285150> != <stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv object at 0x0000026FC7ABB850> warnings.warn("Training and eval env are not of the same type" f"{self.training_env} != {self.eval_env}") Traceback (most recent call last): File "D:\File\Dissertation\深度学习\FR5_Reinforcement-learning-master\Fr5_train.py", line 171, in
model.learn(total_timesteps=TIMESTEPS,
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\ppo\ppo.py", line 315, in learn
return super().learn(
^^^^^^^^^^^^^^
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 300, in learn
continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\on_policy_algorithm.py", line 224, in collect_rollouts
rollout_buffer.add(
File "C:\Users\24270\AppData\Roaming\Python\Python311\site-packages\stable_baselines3\common\buffers.py", line 470, in add
self.observations[self.pos] = np.array(obs)