### Question :

OpenAI’s REINFORCE and actor-critic example for reinforcement learning has the following code:

```
policy_loss = torch.cat(policy_loss).sum()
```

```
loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()
```

One is using `torch.cat`

, the other uses `torch.stack`

.

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.**

##
Answer #1:

`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) and `torch.stack([A, B], dim=0)`

will be of shape (2, 3, 4).

##
Answer #2:

The original answer lacks a good example that is self-contained so here it goes:

```
# %%
```import torch

# stack vs cat

# cat "extends" a list in the given dimension e.g. adds more rows or columns

x = torch.randn(2, 3)

print(f'{x.size()}')

# add more rows (thus increasing the dimensionality of the column space to 2 -> 6)

xnew_from_cat = torch.cat((x, x, x), 0)

print(f'{xnew_from_cat.size()}')

# add more columns (thus increasing the dimensionality of the row space to 3 -> 9)

xnew_from_cat = torch.cat((x, x, x), 1)

print(f'{xnew_from_cat.size()}')

print()

# stack serves the same role as append in lists. i.e. it doesn't change the original

# vector space but instead adds a new index to the new tensor, so you retain the ability

# get the original tensor you added to the list by indexing in the new dimension

xnew_from_stack = torch.stack((x, x, x, x), 0)

print(f'{xnew_from_stack.size()}')

xnew_from_stack = torch.stack((x, x, x, x), 1)

print(f'{xnew_from_stack.size()}')

xnew_from_stack = torch.stack((x, x, x, x), 2)

print(f'{xnew_from_stack.size()}')

# default appends at the from

xnew_from_stack = torch.stack((x, x, x, x))

print(f'{xnew_from_stack.size()}')

print('I like to think of xnew_from_stack as a "tensor list"" that you can pop from the front')

```
```