godot_rl_agents
Gymnasium
godot_rl_agents | Gymnasium | |
---|---|---|
5 | 12 | |
748 | 5,759 | |
- | 5.2% | |
9.1 | 9.3 | |
21 days ago | 3 days ago | |
Python | Python | |
MIT License | MIT License |
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godot_rl_agents
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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.
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An orb learns to dodge obstacles, collect other orbs and reach the end platform by itself using PPO RL AI
1. You will need to setup a virtual environment in python, install the module with pip, and run the "gdrl" command. Then to use it with godot, simply add a "sync" node provided with the plugin to the tree and it will take care of all your agents and their communication with the python module. The agent needs to be in the "AGENT" group and have 3 compulsory variables and some compulsory functions. You can check it out here: Custom Environment .
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Godot Engine Release Management: 4.0 and beyond
I really want to play with Godot RL Agents but I want to do vision based learning and that’s a bit down their road map. If anyone wants to contribute to add that feature I’d love you!
https://github.com/edbeeching/godot_rl_agents
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Build custom 3D gridworld environments
Check out Godot RL Agents as an alternative to Unity.
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[P] Introducing Godot RL Agents
We are proud to announce the release v0.1 of the Godot RL Agents framework, a Deep Reinforcement Learning interface for the Godot Game Engine.
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?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
flake8 - The official GitHub mirror of https://gitlab.com/pycqa/flake8
Miniworld - Simple and easily configurable 3D FPS-game-like environments for reinforcement learning
Flake8-pyproject - Flake8 plug-in loading the configuration from pyproject.toml
episodic-transformer-memory-ppo - Clean baseline implementation of PPO using an episodic TransformerXL memory
ruff - An extremely fast Python linter and code formatter, written in Rust.
DI-engine - OpenDILab Decision AI Engine
agents - TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Visual Studio Code - Visual Studio Code
avalon - A 3D video game environment and benchmark designed from scratch for reinforcement learning research