I am using mixed input observations (i.e. parameters and images)
If I understand this page well:
https://docs.ray.io/en/latest/rllib/rllib-rlmodule.html
It means PPO and Impala are already capable of handling images via ComplexNet?
Not sure if the determination to use ComplexNet is done automatically.
I had been playing with PPO with images of shape (w, h, 3) without an issue (even though I cannot assert that it was threating it as a ComplexNet).
I now tried out Impala, and no matter what I tried I seem to get this error:
(RolloutWorker pid=17488) File "c:\Users\user_x\anaconda3\envs\GTRay2.6.3_torch_tb\lib\site-packages\ray\rllib\utils\policy.py", line 142, in create_policy_for_framework
(RolloutWorker pid=17488) return policy_class(observation_space, action_space, merged_config)
(RolloutWorker pid=17488) File "c:\Users\user_x\anaconda3\envs\GTRay2.6.3_torch_tb\lib\site-packages\ray\rllib\algorithms\impala\impala_torch_policy.py", line 217, in __init__
(RolloutWorker pid=17488) TorchPolicyV2.__init__(
(RolloutWorker pid=17488) File "c:\Users\user_x\anaconda3\envs\GTRay2.6.3_torch_tb\lib\site-packages\ray\rllib\policy\torch_policy_v2.py", line 96, in __init__
(RolloutWorker pid=17488) model, dist_class = self._init_model_and_dist_class()
...
(RolloutWorker pid=17488) return self._conv_forward(input, self.weight, self.bias)
(RolloutWorker pid=17488) File "c:\Users\user_x\anaconda3\envs\GTRay2.6.3_torch_tb\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
(RolloutWorker pid=17488) return F.conv2d(input, weight, bias, self.stride,
(RolloutWorker pid=17488) RuntimeError: Calculated padded input size per channel: (1 x 11). Kernel size: (11 x 11). Kernel size can't be greater than actual input size
That to me suggests that it “detected” to use a CNN but also did not pic the axis correctly?
Or is it attempting to apply CNN to it all?
Is there a way for me to force ComplexNet?