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2 changes: 1 addition & 1 deletion torchrl/modules/models/models.py
Original file line number Diff line number Diff line change
Expand Up @@ -955,7 +955,7 @@ class DuelingCnnDQNet(nn.Module):
>>> cnn_kwargs = {
... 'num_cells': [32, 64, 64],
... 'strides': [4, 2, 1],
... 'kernels': [8, 4, 3],
... 'kernel_sizes': [8, 4, 3],
... }

mlp_kwargs (dict or list of dicts, optional): kwargs for the advantage
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4 changes: 2 additions & 2 deletions tutorials/sphinx-tutorials/getting-started-3.py
Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@
indices = buffer.extend(data)

#################################
# We can check that the buffer now has the same number of elements than what
# We can check that the buffer now has the same number of elements as what
# we got from the collector:

assert len(buffer) == collector.frames_per_batch
Expand All @@ -174,7 +174,7 @@
# Next steps
# ----------
#
# - You can have look at other multirpocessed
# - You can have look at other multiprocessed
# collectors such as :class:`~torchrl.collectors.collectors.MultiSyncDataCollector` or
# :class:`~torchrl.collectors.collectors.MultiaSyncDataCollector`.
# - TorchRL also offers distributed collectors if you have multiple nodes to
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2 changes: 1 addition & 1 deletion tutorials/sphinx-tutorials/rb_tutorial.py
Original file line number Diff line number Diff line change
Expand Up @@ -168,7 +168,7 @@
buffer_lazytensor = ReplayBuffer(storage=LazyTensorStorage(size))

######################################################################
# Let us create a batch of data of size ``torch.Size([3])` with 2 tensors
# Let us create a batch of data of size ``torch.Size([3])`` with 2 tensors
# stored in it:
#

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