Gymnasium VS Flake8

"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.

Automating code checks with static code analysis allows us to enforce code styling effectively. By integrating tools into our workflow, we can identify errors at an early stage, while coding instead of blocking us at the end. For instance, flake8 checks Python code for style and errors, eslint performs similar checks for JavaScript, and prettier automatically formats code to maintain consistency.

Flake8. This library is a wrapper around pycodestyle (PEP8), pyflakes, and Ned Batchelder’s McCabe script. It is a great toolkit for checking your code base against coding style (PEP8), programming errors (like SyntaxError, NameError, etc) and to check cyclomatic complexity.

Flake8 combines the functionalities of the PyFlakes, pycodestyle, and McCabe libraries. It provides a streamlined approach to code linting by detecting coding errors, enforcing style conventions, and measuring code complexity.

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

repos: - repo: https://github.com/psf/black rev: 21.7b0 hooks: - id: black language_version: python3.8 - repo: https://github.com/PyCQA/flake8 rev: 3.9.2 hooks: - id: flake8

https://github.com/psf/black it’s a PEP8 compliant formatter for Python codebases. If you don’t like auto formatting files you can use https://github.com/PyCQA/flake8 it just lists out all of the style issues so you can fix them manually.

I have no stake in that, but my observation is that the actual discussion appears to have both supporters and detractors rather than overwhelming support. Either way, it has nothing to do with whether or not it is realistic to say that Ruff is the "one Python linter to rule them all".