Describe the bug

When depth and num_cells are both not specified, MLP will by default create a fully-connected neural network with three hidden layers, each with 32 neurons.

This is somewhat surprising, as I would imaging leaving depth and num_cells unspecified would lead to a neural network with no hidden layer at all. Not sure why use depth=3 and num_cells=32 as default here :)

This is not really a bug report, just IMHO a weird design choice which is also not documented. Would appreciate if we can discuss and reconsider this.

To Reproduce

>>> from torchrl.modules import MLP
>>> MLP(in_features=1024, out_features=512)
MLP(
  (0): Linear(in_features=1024, out_features=32, bias=True)
  (1): Tanh()
  (2): Linear(in_features=32, out_features=32, bias=True)
  (3): Tanh()
  (4): Linear(in_features=32, out_features=32, bias=True)
  (5): Tanh()
  (6): Linear(in_features=32, out_features=512, bias=True)
)

Expected behavior

>>> MLP(in_features=1024, out_features=512)
MLP(
  (0): Linear(in_features=1024, out_features=512, bias=True)
)

System info

• Python 3.10.0
• TorchRL 0.3.1

Reason and Possible fixes

Checklist

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