open-source-rover VS Gymnasium

Naonosaur: https://github.com/rnanosaur/nanosaur. If you would like something larger and with a bit more off the shelf components then jpl open source rover has seem quite a bit of updates recently: https://github.com/nasa-jpl/open-source-rover

"Our position is that the license chosen overrides the disclaimer. We have approval to license this software under the Apache License, version 2, which allows others to do what they wish (even build commercial software on it). That should supersede the attached disclaimer which are artifacts of an evolving OSS release process that we are trying to improve."

https://github.com/nasa-jpl/open-source-rover/issues/2

How much money do you have? https://opensourcerover.jpl.nasa.gov/

Another way to go if you wanted to go from scratch is the official nasa rover build https://github.com/nasa-jpl/open-source-rover which I've only heard good things about

https://kitspace.org

Kitspace is a place to share ready to order open hardware electronics projects. You can order the the circuit board and all components for a project with just a few clicks.

I’m seeking a someone to help improve the documentation of open source hardware electronics projects by asking creators whether they would like to mirror their project to Kitspace.

The way to do this is to send Github pull-requests to projects (some examples: https://github.com/Ottercast/OtterCastAudioV2/pull/8, https://github.com/gregdavill/d20-hardware/pull/2 and https://github.com/nasa-jpl/open-source-rover/pull/180). As you can see in the examples it requires clear communication and some familiarity with electronics parts and making bills of materials. Most importantly it requires a mindset to try and help and improve the project in question through documentation rather than badgering people to add it to the site. Familiarity with Git, Github and at least one electronics CAD software (KiCad, Eagle etc.) is also helpful.

Naturally this gig is fully remote and the number of hours and length of contract you would like to commit to is up to you. Time zone is not dictated either, though some overlap with UTC+1, for the occasional meeting, would be ideal.

This is a fully open source project and does not turn a profit hence the budget is limited. I’m looking for people that can accept a rate of USD $20 an hour.

Please send applications, including a CV, via email to [email protected]. If you have any questions feel free to ask on this forum below or via email or private message.

"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.