godot-rl 0.4.1

A Deep Reinforcement Learning package for the Godot game engine

Godot RL Agents

The 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 and Machine Learning algorithms running in Python
• Wrappers for three well known rl frameworks: StableBaselines3, Sample Factory and Ray RLLib
• 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://user-images.githubusercontent.com/7275864/140730165-dbfddb61-cc90-47c7-86b3-88086d376641.mp4

Contents

1. Motivation
2. Installation
3. Examples
4. Creating custom environments
5. Roadmap
6. FAQ
7. Licence
8. Citing Godot RL Agents
9. Acknowledgments
10. References

Motivation

Over the next decade advances in AI algorithms, notably in the fields of Machine Learning and Deep Reinforcement Learning, are primed to revolutionize the Video Game industry. Customizable enemies, worlds and story telling will lead to diverse gameplay experiences and new genres of games. Currently the field is dominated by large organizations and pay to use engines that have the budget to create such AI enhanced agents. The objective of the Godot RL Agents package is to lower the bar of accessability so that game developers can take their idea from creation to publication end-to-end with an open source and free package.

Installation

Godot RL Agents has been tested on Linux and Windows and should work out of the box, we recommend using a virtual environment. pip install godot-rl

In order to perform training we support 3 different backends in Godot RL Agents:

• Stable Baselines 3 pip install godot-rl[sb3]
• Sample Factory pip install godot-rl[sf]
• Ray rllib pip install godot-rl[rllib]

If you are having issues with the installation, please refer to our FAQ section or raise an issue.

Examples

We have created 5 examples of varying complexity. All environments are hosted in separate repo and can be downloaded with

gdrl.env_from_hub -r edbeeching/godot_rl_<ENV_NAME>

Note you may need to set execution permissions on the binary with:

chmod +x examples/godot_rl_<ENV_NAME>/bin/<ENV_NAME>.x86_64 

Simple environments

BallChase

https://user-images.githubusercontent.com/7275864/209159206-a7896103-5492-4a62-8a2f-bad1e3741dae.mp4

Stable Baselines 3: (TODO)

Sample Factory:

• Train a model from scratch:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_BallChase/bin/BallChase.x86_64 --num_workers=10 --experiment=BallChase --viz  --speedup=8 --batched_sampling=True

• Download a pretrained checkpoint from the HF hub:

python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_BallChase

• Visualize a trained model:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_BallChase/bin/BallChase.x86_64 --num_workers=1 --experiment=BallChase --viz --eval --batched_sampling=True

• Load a pretrained checkpoint:

python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_BallChase

• Upload a model to the hub:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_BallChase/bin/BallChase.x86_64 --num_workers=1 --experiment=BallChase --viz --eval --batched_sampling=True --speedup=8 --push_to_hub --hf_repository=<HF_USERNAME>/sample_factory_BallChase

FlyBy

https://user-images.githubusercontent.com/7275864/209160025-0781537e-ff37-427d-bb32-753299b30510.mp4

Stable Baselines 3: (TODO)

Sample-factory:

• Train a model from scratch:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FlyBy/bin/FlyBy.x86_64 --num_workers=10 --experiment=FlyBy --viz --speedup=8 --batched_sampling=True

• Download a pretrained checkpoint from the HF hub:

python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_FlyBy

• Visualize a trained model:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FlyBy/bin/FlyBy.x86_64 --num_workers=1 --experiment=FlyBy --viz --eval --batched_sampling=True

• Upload a model to the hub:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FlyBy/bin/FlyBy.x86_64 --num_workers=1 --experiment=FlyBy --viz --eval --batched_sampling=True --speedup=8 --push_to_hub --hf_repository=<HF_USERNAME>/sample_factory_FlyBy --max_num_frames=10000

JumperHard

https://user-images.githubusercontent.com/7275864/209160056-b96ed6f4-3b8b-467a-997d-7e4833e99025.mp4

Stable Baselines 3: (TODO)

Sample-factory:

• Train a model from scratch:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --num_workers=10 --experiment=JumperHard01 --viz --batched_sampling=True --speedup=8

• Download a pretrained checkpoint from the HF hub:

python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_JumperHard

• Visualize a trained model: gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --num_workers=1 --experiment=JumperHard --viz --eval --batched_sampling=True
• Upload a model to the hub:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --num_workers=1 --experiment=JumperHard --viz --eval --batched_sampling=True --speedup=8 --push_to_hub --hf_repository=<HF_USERNAME>/sample_factory_JumperHard --max_num_frames=10000

Advanced Environments

We highly recommend training these environments on a compute cluster. As they take several hours / GPUs to converge to a decent policy.

Racer (TODO)

Stable Baselines 3: (TODO)

Sample-factory:

• Train a model from scratch:
• Download a pretrained checkpoint from the HF hub:
• Visualize a trained model:

Team FPS (experimental)

https://user-images.githubusercontent.com/7275864/209160117-cd95fa6b-67a0-40af-9d89-ea324b301795.mp4

Stable Baselines 3: (TODO)

Sample-factory:

• Train a model from scratch

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FPS/bin/FPS.x86_64 --num_workers=10 --experiment=FPS --viz --batched_sampling=True --speedup=8 --num_workers=80 --batched_sampling=False --num_policies=4 --with_pbt=True --pbt_period_env_steps=1000000 --pbt_start_mutation=1000000 --batch_size=2048 --num_batches_per_epoch=2 --num_epochs=2 --learning_rate=0.00005 --exploration_loss_coef=0.001 --lr_schedule=kl_adaptive_epoch --lr_schedule_kl_threshold=0.08 --use_rnn=True --recurrence=32

• Download a pretrained checkpoint from the HF hub:

python -m sample_factory.huggingface.load_from_hub -r edbeeching/sample_factory_FPS

• Visualize a trained model:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FPS/bin/FPS.x86_64 --num_workers=1 --experiment=FPS --viz --eval --batched_sampling=True

• Upload a model to the hub:

gdrl --trainer=sf --env=gdrl --env_path=examples/godot_rl_FPS/bin/FPS.x86_64 --num_workers=1 --experiment=FPS --viz --eval --batched_sampling=True --speedup=8 --push_to_hub --hf_repository=<HF_USERNAME>/sample_factory_FPS_ --max_num_frames=10000

More details about the environments can be found in Example environments

Training on a cluster

The above training commands should all work on a headless cluster, just remove the --viz flag.

Downloading the Godot Editor

The Godot 4 Game Engine is lightweight at around 50 MB, you can find the beta version on their website. Alternatively you can download the version used to create these environments (Godot 4 Beta 4) using the command: gdrl.download_editor The should work on Linux, Mac and Windows. But has not been extensively tested.

Running Environments in the Editor

Godot RL Agents envs can be run interactively in the editor, for easy debugging and testing of new ideas. The folling command will attempt to connect to an editor and step through 1000 random actions.

gdrl.interactive

Creating custom environments

(Doc is WIP, raise an issue if anything is unclear) Once you have studied the example environments, you can follow the instructions in Custom environments in order to make your own.

Roadmap

We have number features that will soon be available in versions 0.4.0 Refer to the Roadmap for more information.

FAQ

1. 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.

2. 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.
3. When will you be providing Mac support? I would like to provide this ASAP but I do not own a mac so I cannot perform any manual testing of the codebase.
4. Can you help with my game project? If the game example do not provide enough information, reach out to us on github and we may be able to provide some advice.
5. 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 for details.

"Cartoon Plane" (https://skfb.ly/UOLT) by antonmoek is licensed under Creative Commons Attribution (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 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.

References