Gymnasium VS pyupgrade

"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :

[RL, MuZero reduxxxx ]

> Farama-Foundation/Gymnasium is a fork of OpenAI/gym and it has support for additional Environments like MuJoCo: https://github.com/Farama-Foundation/Gymnasium#environments

> Farama-Foundatiom/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium

https://github.com/Farama-Foundation/Gymnasium#environments

Farama-Foundatiom/MO-Gymnasiun:

https://github.com/Farama-Foundation/Gymnasium - A framework Python library to build and train your own AI to play games

The only change in the environment from the one here is the reward function which is given its return value using the following code snippet (replacing lines 648-672 in the above url):

This may be useful: https://github.com/Farama-Foundation/Gymnasium

Here are all the environments. So for example, if instead of Hopper-v2 you want the acrobat environment from classic control you can write: env = gym.make('Acrobot-v1')

This release also includes a large number of documentation updates, minor bug fixes, and other minor improvements; the full release notes are available here if you’d like to learn more: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.28.0.

Thanks! It really depends on the task that you want to implement. But in general, sticking to the standard gymnasium API is important. If you want to implement a 2D environment then PyGame is promising. If it's more like a game, check out Unity ML-Agents or Godot RL Agents. Anything simpler can also be just pure python code. You also need to carefully design your observation space, action space and reward function. My advice is to explore design choices of related environments.

You can read the release notes here: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.27.0. You can upgrade from 0.26 without any changes unless you're doing something very uncommon; this is how releases will generally be going forward.

pyupgrade and flynt are examples of tools that modify your code base from earlier python versions into the newest python syntax, rewriting all string formats into f-strings and similar things.

pyupgrade - A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.

He made yesqa and pyupgrade (among others), and also works on flake8. His main job is for https://sentry.io/.

That's a decade to make a 30 second change. Add something like https://github.com/asottile/pyupgrade to your pre-commit hooks and you won't even need 25 of those seconds.

There is also pyupgrade: https://github.com/asottile/pyupgrade

pyupgrade is a useful tool that can help you find some of these things.

and last a little plug or two -- because I do this all for free and despite millions benefiting I receive zero proportional benefit from the maintenance work I put in -- consider sponsoring or maybe check out pre-commit.ci which would have automatically fixed this problem for you a year and a half ago

Running https://github.com/adamchainz/django-upgrade with https://github.com/asottile/pyupgrade recursively will give an idea about how much work is there on Django side. Still, there may have dependency on third party libraries (both django+python). Another thing to consider is which role Django performing here, serving APIs or html views. As good test coverage is already there, you are on lucky side.

Such goodness here and even points to an interesting project I’d never heard of for automated “de-deprecation”

https://github.com/asottile/pyupgrade

I do would like to recommend pyupgrade. Just pip install as expected and then run pyupgrade --py310-plus to drag your code kicking and screaming into $CURRENT_YEAR. Or at least into whatever version you're using :)