JOjonbaer·Oct 8, 2024Submission
1 points · Open discussionFarama-Foundation
Gymnasium
Python2023–2024
Farama-Foundation / Gymnasium
Gymnasium
A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
3Distinct people
5Mentions
0Explicit recommenders
12.8KGitHub stars
What to know before exploring
What it is
A standard API for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month5 dated mentions
Observed mentions: 2023-09-12 01:04:44 UTC to 2024-10-08 16:27:46 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Sep 2023 | 2023-09-01 00:00:00 UTC | 2023-10-01 00:00:00 UTC | 2 | 0 | 2 |
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 1 | 0 | 1 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2024 | 2024-04-01 00:00:00 UTC | 2024-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2024 | 2024-05-01 00:00:00 UTC | 2024-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2024 | 2024-06-01 00:00:00 UTC | 2024-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2024 | 2024-07-01 00:00:00 UTC | 2024-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2024 | 2024-08-01 00:00:00 UTC | 2024-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2024 | 2024-09-01 00:00:00 UTC | 2024-10-01 00:00:00 UTC | 1 | 0 | 1 |
| Oct 2024 | 2024-10-01 00:00:00 UTC | 2024-11-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
5 sourcesI'm a novice at machine learning, but Open AI made a python library for reinforcement learning in video games, and a fork of it is still actively maintained [1]. It's been a few years, but I remember being able to get it up and running in a day or two (maybe a weekend). It used the Retroarch emulator, which is compatible with a huge number of emulators and consoles.
https://github.com/Farama-Foundation/Gymnasium
There also SethBling's excellent video on YouTube about machine learning specifically with Super Mario World:
https://www.youtube.com/watch?v=qv6UVOQ0F44
I encourage you to give it a try! I feel that video games are a bit underrated by current AI buzz, I think there's lots of potential for a machine to learn a skill through playing a game. And lots of potential for a game being selected or even created with the goal of teaching a
Read the rest
specific skill. However at that point maybe it's better to forego the audio and visuals and speak to the machine in text or with pure data.
On the other hand, I have seen a video about convolutional neural networks that feed on each pixel of an image. So perhaps training with sound data, or with the pixels of a spectrogram, could have some positive results. And certainly I would be amused to see a game played in time with music, or possibly even dancing with a songs melody, harmony, and story as well.
Anything that's ever been created by humans, existed first in the imagination of a human brain. And you've got one of those. A mental vision pursued and brought from the mind into physical reality is a beautiful thing, a gift for all of humanity in my eyes. I think it's quite worthwhile. But that's just my perspective. Thank you for sharing your imagination. Have a nice day
"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :
[RL, ..., MuZero unplugged w/ PyTorch ]
> 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
The Mario64 game can be won with a minimal number of stars;
> Farama-Foundation/MO-Gymnasiun: "Multi-objective Gymnasium environments for reinforcement learning": https://github.com/Farama-Foundation/MO-Gymnasium
Ghidra may or may not be useful for e.g. gadgets with mario64
Return-oriented programming > On the x86-architecture:
https://en.wikipedia.org/wiki/Return-oriented_programming#On...
In FPGA N64"Show HN: Ghidra Plays Mario" (2023) https://news.ycombinator.com/item?id=37475761 :
[RL, ..., MuZero unplugged w/ PyTorch ]
> 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
Idea: Generate code like BlenderGPT to generate drone rover sim scenarios and environments like the Moon and Mars
TIL about teh "Bush Winch" which mounts to a tire for off-road vehicle recovery. Note the winch line blankets, snatch blocks, and tree protectors in this video about off-road vehicle recovery:
https://youtu.be/OXxLh8shMu8?si=bv59t8T1or07-K7l
Also RIL about o3de, which does PhysX:
Read the rest
The latest on winning Atari with RL+ FWIU:
https://github.com/openai/retro:
> Gym Retro lets you turn classic video games into Gym environments for reinforcement learning and comes with integrations for ~1000 games. It uses various emulators that support the Libretro API, making it fairly easy to add new emulators.
.nes is listed in the supported ROM types:
https://retro.readthedocs.io/en/latest/integration.html#supp...
> Integrating a Game: To integrate a game you need to define a done condition and a reward function. The done condition lets Gym Retro know when to end a game session, while the reward function provides a simple numeric goal for machine learning agents to maximize.
> To define these, you find variables from the game’s memory, such as the player’s current score and lives remaining, and use those to create the done conditio
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n and reward function. An example done condition is when the `lives` variable is equal to 0, an example reward function is the change in the `score` variable.
PPO Proximal Policy Optimization and OpenAI/baselines:
https://retro.readthedocs.io/en/latest/getting_started.html#...
MuZero: https://en.wikipedia.org/wiki/MuZero
MuZero-unplugged with PyTorch:
https://github.com/DHDev0/Muzero-unplugged
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