Metadata-Version: 2.1
Name: godot-rl
Version: 0.4.8
Summary: A Deep Reinforcement Learning package for the Godot game engine
Author: Edward Beeching
Author-email: Edward Beeching <edbeeching@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/pypa/godot_rl_agents
Project-URL: Bug Tracker, https://github.com/pypa/godot_rl_agents/issues
Platform: unix
Platform: linux
Platform: osx
Platform: cygwin
Platform: win32
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: sb3
Provides-Extra: sf
Provides-Extra: rllib
Provides-Extra: clean-rl
Provides-Extra: all
License-File: LICENSE

# Godot RL Agents

Feel free to join our [Discord](https://discord.gg/HMMD2J8SxY) for help and discussions about Godot RL Agents.

Godot RL Agents is a fully Open Source package that allows video game creators, AI researchers and hobbyists the opportunity to learn complex behaviors for their Non Player Characters or agents.
This repository provides:

- An interface between games created in the [Godot Engine](https://godotengine.org/) and Machine Learning algorithms running in Python
- Wrappers for four well known rl frameworks: [StableBaselines3](https://stable-baselines3.readthedocs.io/en/master/), [Sample Factory](https://www.samplefactory.dev/), [Ray RLLib](https://docs.ray.io/en/latest/rllib-algorithms.html) and [CleanRL](https://github.com/vwxyzjn/cleanrl).
- Support for memory-based agents, with LSTM or attention based interfaces
- Support for 2D and 3D games
- A suite of AI sensors to augment your agent's capacity to observe the game world
- Godot and Godot RL Agents are completely free and open source under the very permissive MIT license. No strings attached, no royalties, nothing.

You can find out more about Godot RL agents in our AAAI-2022 Workshop [paper](https://arxiv.org/abs/2112.03636).

[https://user-images.githubusercontent.com/7275864/140730165-dbfddb61-cc90-47c7-86b3-88086d376641.mp4](https://user-images.githubusercontent.com/7275864/140730165-dbfddb61-cc90-47c7-86b3-88086d376641.mp4)

## Quickstart Guide

This quickstart guide will get you up and running using the Godot RL Agents library with the StableBaselines3 backend, as this supports Windows, Mac and Linux. We suggest starting here and then trying out our Advanced tutorials when learning more complex agent behaviors.

### Installation and first training

1. Install the Godot RL Agents library: (if you are new to python, pip and conda, read this [guide](https://www.machinelearningplus.com/deployment/conda-create-environment-and-everything-you-need-to-know-to-manage-conda-virtual-environment/))

```bash
pip install godot-rl
```

1. Download one, or more of [examples](https://github.com/edbeeching/godot_rl_agents_examples), such as BallChase, JumperHard, FlyBy.

```bash
gdrl.env_from_hub -r edbeeching/godot_rl_JumperHard 
```

1. Train and visualize 

```bash
gdrl --env=gdrl --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --viz
```

### In editor interactive training

You can also train an agent in the Godot editor, without the need to export the game executable.

1. Download the Godot 4 Game Engine from [https://godotengine.org/](https://godotengine.org/)
2. Open the engine and import the JumperHard example in `examples/godot_rl_JumperHard`
3. Start in editor training with: `gdrl` 

### Creating a custom environment

There is a dedicated tutorial on creating custom environments [here](docs/CUSTOM_ENV.md). We recommend following this tutorial before trying to create your own environment.

If you face any issues getting started, please reach out on our discord or raise a github issue.

## Advanced usage
[https://user-images.githubusercontent.com/7275864/209160117-cd95fa6b-67a0-40af-9d89-ea324b301795.mp4](https://user-images.githubusercontent.com/7275864/209160117-cd95fa6b-67a0-40af-9d89-ea324b301795.mp4)


Please ensure you have successfully completed the quickstart guide before following this section.

Godot RL Agents supports 4 different RL training frameworks, the links below detail a more in depth guide of how to use a particular backend:

- [StableBaselines3](docs/ADV_STABLE_BASELINES_3.md) (Windows, Mac, Linux)
- [SampleFactory](docs/ADV_SAMPLE_FACTORY.md) (Mac, Linux)
- [CleanRL](docs/ADV_CLEAN_RL.md) (Windows, Mac, Linux)
- [Ray rllib](docs/ADV_RLLIB.md) (Windows, Mac, Linux)

## FAQ

### Why have we developed Godot RL Agents?

The objectives of the framework are to:
- Provide a free and open source tool for Deep RL research and game development.
- Enable game creators to imbue their non-player characters with unique behaviors.
- Allow for automated gameplay testing through interaction with an RL agent.
### How can I contribute to Godot RL Agents?

Please try it out, find bugs and either raise an issue or if you fix them yourself, submit a pull request.

### When will you be providing Mac support?

This should now be working, let us know if you have any issues.

### Can you help with my game project?

If the README and docs here not provide enough information, reach out to us on github and we may be able to provide some advice.

### How similar is this tool to Unity ML agents?

We are inspired by the the Unity ML agents toolkit and aims to be a more compact, concise ad hackable codebase, with little abstraction.

# Licence

Godot RL Agents is MIT licensed. See the [LICENSE file](https://www.notion.so/huggingface2/LICENSE) for details.

"Cartoon Plane" ([https://skfb.ly/UOLT](https://skfb.ly/UOLT)) by antonmoek is licensed under Creative Commons Attribution ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

# Citing Godot RL Agents

```
@article{beeching2021godotrlagents,
  author={Beeching, Edward and Dibangoye, Jilles and
    Simonin, Olivier and Wolf, Christian},
title = {Godot Reinforcement Learning Agents},
journal = {{arXiv preprint arXiv:2112.03636.},
year = {2021},
}
```

# Acknowledgments

We thank the authors of the Godot Engine for providing such a powerful and flexible game engine for AI agent development.
We thank the developers at Sample Factory, Clean RL, Ray and Stable Baselines for creating easy to use and powerful RL training frameworks.
We thank the creators of the Unity ML Agents Toolkit, which inspired us to create this work.
