fix pytorch checkpointing. add tensors in Normalizer as parameter

ml-agents/mlagents/trainers/torch/encoders.py

 class Normalizer(nn.Module):
     def __init__(self, vec_obs_size: int):
         super().__init__()
-        self.normalization_steps = torch.tensor(1)
-        self.running_mean = torch.zeros(vec_obs_size)
-        self.running_variance = torch.ones(vec_obs_size)
+        self.normalization_steps = nn.Parameter(torch.tensor(1), requires_grad=False)
+        self.running_mean = nn.Parameter(torch.zeros(vec_obs_size), requires_grad=False)
+        self.running_variance = nn.Parameter(torch.ones(vec_obs_size), requires_grad=False)
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         normalized_state = torch.clamp(
         new_variance = self.running_variance + (
             input_to_new_mean * input_to_old_mean
         ).sum(0)
-        self.running_mean = new_mean
-        self.running_variance = new_variance
-        self.normalization_steps = total_new_steps
+        self.running_mean.data = new_mean.data
+        self.running_variance.data = new_variance.data
+        self.normalization_steps.data = total_new_steps.data
 
     def copy_from(self, other_normalizer: "Normalizer") -> None:
         self.normalization_steps.data.copy_(other_normalizer.normalization_steps.data)
         self.normalizer: Optional[Normalizer] = None
         super().__init__()
         self.layers = [nn.Linear(input_size, hidden_size)]
+        print('-'*10, normalize, '-'*10)
         if normalize:
             self.normalizer = Normalizer(input_size)