Hello PyTorch community!

I have a question regarding the use of different normalisation ranges and their benefits in training a neural network model.

Currently, I have normalised my inputs to the neural network to be between 1.0 and 10.0 (reinforcement learning), but I was wondering if a normalisation range of between -1.0 and 1.0 or even 0.0 and 1.0 will improve the training speed and stability.

Additional information: The neural network uses the tanh activation function (PPO) with 3 layers of 64 nodes each.

This is the normalisation code block I am using

```
from sklearn import preprocessing
def normalisation_of_vector(self, vector, lower_bound = 1, upper_bound = 10):
'''
Might change to between 0 and 1 or -1 and 1
'''
np_list = np.array(vector).reshape(-1,1)
normalization_scaler = preprocessing.MinMaxScaler(feature_range=(lower_bound, upper_bound))
normalized_np_list = normalization_scaler.fit_transform(np_list)
normalized_literal_list = normalized_np_list.tolist()
flattened_list = functools.reduce(operator.concat, normalized_literal_list)
rounded_flattened_list = list(np.around(np.array(flattened_list), 2))
return rounded_flattened_list
```

Any help is greatly appreciated!