What's the difference between torch.stack() and torch.cat() functions?
OpenAI's REINFORCE and actor-critic example for reinforcement learning has the following code:
REINFORCE:
policy_loss = torch.cat(policy_loss).sum()

actor-critic:
loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()

One is using torch.cat, the other uses torch.stack, for similar use cases.
As far as my understanding goes, the doc doesn't give any clear distinction between them.
I would be happy to know the differences between the functions.

stack

Concatenates sequence of tensors along a new dimension.

cat

Concatenates the given sequence of seq tensors in the given dimension.

So if A and B are of shape (3, 4):

torch.cat([A, B], dim=0) will be of shape (6, 4)
torch.stack([A, B], dim=0) will be of shape (2, 3, 4)

