Add reinforcement learning example for Fabric

[belerico]

What does this PR do?

This PR introduces a first example of Reinforcement Learning training accelerated by Fabric. In particular, the Proximal Policy Optimization (PPO) algorithm is implemented. As for the other examples, two scripts are given: one that runs the optimization with raw PyTorch, while the other one employing Fabric.

Does your PR introduce any breaking changes? If yes, please list them.

Before submitting

• Was this discussed/approved via a GitHub issue? (not for typos and docs)
• Did you read the contributor guideline, Pull Request section?
• Did you make sure your PR does only one thing, instead of bundling different changes together?
• Did you make sure to update the documentation with your changes? (if necessary)
• Did you write any new necessary tests? (not for typos and docs)
• Did you verify new and existing tests pass locally with your changes?
• Did you update the CHANGELOG? (not for typos, docs, test updates, or internal minor changes/refactorings)

PR review

Anyone in the community is free to review the PR once the tests have passed.
Before you start reviewing make sure you have read Review guidelines. In short, see the following bullet-list:

• Is this pull request ready for review? (if not, please submit in draft mode)
• Check that all items from Before submitting are resolved
• Make sure the title is self-explanatory and the description concisely explains the PR
• Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?

Make sure you had fun coding 🙃

[awaelchli]

Amazing. Thank you for contributing this example @belerico

[lantiga]

Looks great, small tweak to the intro

[lantiga]

Great job @belerico !

[belerico]

Thank you all!

[Borda]

Sweet, nice first contribution! 🎉