Fix tensorboad video slow numpy->torch conversion

[NickLucche]

Hey,

this PR is a small docs fix to the speed issue highlighted here https://github.com/DLR-RM/stable-baselines3/pull/196#issuecomment-714656995, which also shows up as a warning now in more recent torch versions :UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:275.).

You can also test the speed difference yourself with:

from time import time
import numpy as np
import torch

rgb = np.ones((3,256,320))
rgbs = [rgb.copy() for _ in range(100)]

s = time()
a = torch.ByteTensor(rgbs)
print(time()-s)

s = time()
a = torch.from_numpy(np.asarray(rgbs))
print(time()-s)

I get two orders of magnitude of difference in times here.

Description

I create a numpy array first and then let torch use the same storage. np.stack could also be suggested if we prefer being more explicit.

Motivation and Context

Fixes https://github.com/DLR-RM/stable-baselines3/pull/196#issuecomment-714656995.

Types of changes

• [ ] Bug fix (non-breaking change which fixes an issue)
• [ ] New feature (non-breaking change which adds functionality)
• [ ] Breaking change (fix or feature that would cause existing functionality to change)
• [x] Documentation (update in the documentation)

Checklist

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• [ ] I have updated the documentation accordingly.
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• [ ] I have opened an associated PR on the RL-Zoo3 repository (if necessary)
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