Reputation: 176
I am trying to implement SAC with a custom environment in Stable Baselines3 and I keep getting the error in the title. The error occurs with any off policy algorithm not just SAC.
Traceback:
File "<MY PROJECT PATH>\src\main.py", line 70, in <module>
main()
File "<MY PROJECT PATH>\src\main.py", line 66, in main
model.learn(total_timesteps=timesteps, reset_num_timesteps=False, tb_log_name=f"sac_{num_cars}_cars")
File "<MY PROJECT PATH>\venv\lib\site-packages\stable_baselines3\sac\sac.py", line 309, in learn
return super().learn(
File "<MY PROJECT PATH>\venv\lib\site-packages\stable_baselines3\common\off_policy_algorithm.py", line 375, in learn
self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)
File "<MY PROJECT PATH>\venv\lib\site-packages\stable_baselines3\sac\sac.py", line 256, in train
current_q_values = self.critic(replay_data.observations, replay_data.actions)
File "<MY PROJECT PATH>\venv\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "<MY PROJECT PATH>\venv\lib\site-packages\stable_baselines3\common\policies.py", line 885, in forward
return tuple(q_net(qvalue_input) for q_net in self.q_networks)
File "<MY PROJECT PATH>\venv\lib\site-packages\stable_baselines3\common\policies.py", line 885, in <genexpr>
return tuple(q_net(qvalue_input) for q_net in self.q_networks)
File "<MY PROJECT PATH>\venv\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "<MY PROJECT PATH>\venv\lib\site-packages\torch\nn\modules\container.py", line 204, in forward
input = module(input)
File "<MY PROJECT PATH>\venv\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "<MY PROJECT PATH>\venv\lib\site-packages\torch\nn\modules\linear.py", line 114, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 must have the same dtype
Action and observation spaces:
self.action_space = Box(low=-1., high=1., shape=(2,), dtype=np.float)
self.observation_space = Box(
np.array(
[-np.inf] * (9 * 40) + [-np.inf] * 3 + [-np.inf] * 3 + [-np.inf] * 3
+ [0.] + [0.] + [0.] + [-1.] + [0.] * 4 + [0.] * 4 + [0.] * 4,
dtype=np.float
),
np.array(
[np.inf] * (9 * 40) + [np.inf] * 3 + [np.inf] * 3 + [np.inf] * 3
+ [np.inf] + [1.] + [1.] + [1.] + [1.] * 4 + [np.inf] * 4 + [np.inf] * 4,
dtype=np.float
),
dtype=np.float
)
Observations are returned in the step and reset methods as a numpy array of floats.
Is there something I'm missing which is causing this error? If I use one of the environments that come with gym such as pendulum it works fine which is why I think I have a problem with my custom environment.
Thanks in advance for any help and please let me know if more information is required.
Upvotes: 9
Views: 22726
Reputation: 5191
Change the inputs to float32 , default the loader set the type as float64.
inputs = inputs.to(torch.float32)
Upvotes: 21