machine_learning_examples VS stable-baselines

Does that mean that the example I found on the internet is wrong (I think it comes from a DL Course, so I'd imagine it is not wrong)? or does it mean that I am comparing two different things? I guess this has to deal with right and left eigen vectors as u/JanneJM pointed out in her comment?

I've been following a course teaching how to make an attention model for neural machine translation, This is the file inside the repo. I know that I'll have to use certain functions to make the textual input be processed in encodings and tokens, but those functions use certain instances of the model, which I don't know if I should keep or not. If anyone can please take a look and help me out here, it'd be really really appreciated.

As underlined by gold-panda, you can give a try with multiprocessing. I once implemented a version based on what is done in stable_baselines v1 (https://github.com/hill-a/stable-baselines/blob/master/stable_baselines/common/vec_env/subproc_vec_env.py)

Found relevant code at https://github.com/hill-a/stable-baselines + all code implementations here

Depends what you wanna do. Universal answer would be https://stable-baselines.readthedocs.io/

As you will see here it is recommended to augment this safety measure with target kl_divergence, that will ensure even smoother learning and enforce early stopping to prevent learning collapses.

Code for https://arxiv.org/abs/1707.06347 found: https://github.com/hill-a/stable-baselines

I haven't read the code, but stable-baselines doesn't support multi-agent environments (https://github.com/hill-a/stable-baselines/issues/423), so I think they're trying to make learning multi-agent easier with Environment.train().

- https://github.com/hill-a/stable-baselines (Tensorflow)

- tf2 speed: https://github.com/hill-a/stable-baselines/issues/576#issuecomment-573331715