retro
Gymnasium
retro | Gymnasium | |
---|---|---|
6 | 12 | |
3,320 | 5,859 | |
0.4% | 6.8% | |
0.6 | 9.3 | |
3 months ago | 8 days ago | |
C | Python | |
MIT License | MIT License |
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retro
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Show HN: Ghidra Plays Mario
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:
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What has replaced OpenAI Retro Gym?
OpenAI Retro Gym hasn't been updated in years, despite being high profile enough to garner 3k stars. It doesn't even support Python 3.9, and needs old versions of setuptools and gym to get installed.
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Generating expert trajectories for IRL project based on retro NES gym.
The trajectories might not be properly generated (I used a custom wrapper for the retro gym that allows the env to receive human input: Interactive Gym-Retro), so the GAIL agent is just following garbage.
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Can anyone make OpenAI for Age of Empires 2 Definitive Edition?
You can do it yourself. Maybe with retro
- Need help loading a file for use in a Python script (Gym Retro - Brute)
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Trying to create an AI that can play a video game.
Another alternative is OpenAI gyms. For example, this supports NES, SNES, and GB.
Gymnasium
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NASA JPL Open Source Rover That Runs ROS 2
"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
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Show HN: Ghidra Plays Mario
https://github.com/Farama-Foundation/Gymnasium#environments
Farama-Foundatiom/MO-Gymnasiun:
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Are there any AI projects that plays a game for you and learns?
https://github.com/Farama-Foundation/Gymnasium - A framework Python library to build and train your own AI to play games
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Unstable SAC training of sparse-reward task
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):
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Any resources on experiments simulated environments?
This may be useful: https://github.com/Farama-Foundation/Gymnasium
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What's the most challenging Gym environment?
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')
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Gymnasium 0.28 is now released
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.
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TransformerXL + PPO Baseline + MemoryGym
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.
- Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
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[N] Gymnasium 0.27 - the first new version since Gymnasium was announced - is now released. It has almost no breaking changes.
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.
What are some alternatives?
ghidra-tlcs900h - Ghidra processor module for Toshiba TLCS-900/H
flake8 - The official GitHub mirror of https://gitlab.com/pycqa/flake8
ghidra-plays-mario - Playing NES ROMs with Ghidra's PCode Emulator
Flake8-pyproject - Flake8 plug-in loading the configuration from pyproject.toml
Muzero-unplugged - Pytorch Implementation of MuZero Unplugged for gym environment. This algorithm is capable of supporting a wide range of action and observation spaces, including both discrete and continuous variations.
ruff - An extremely fast Python linter and code formatter, written in Rust.
switcher - Gnome Shell extension to switch windows quickly by typing
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
6502_65C02_functional_tests - Tests for all valid opcodes of the 6502 and 65C02 processor
Visual Studio Code - Visual Studio Code
MO-Gymnasium - Multi-objective Gymnasium environments for reinforcement learning
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory