Godot RL Agents

Godot RL Agents

Feel free to join our Discord 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 and Machine Learning algorithms running in Python
- Wrappers for four well known rl frameworks: StableBaselines3, Sample Factory, Ray RLLib and 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://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.
There is also a video tutorial which shows how to install the library and create a custom env.
Installation and first training

Install the Godot RL Agents library. If you are new to Python or not using a virtual environment, it's highly recommended to create one using venv or Conda to isolate your project dependencies.

Once you have set up your virtual environment, proceed with the installation:

bash
pip install godot-rl

Download one, or more of examples, such as BallChase, JumperHard, FlyBy.

bash
gdrl.env_from_hub -r edbeeching/godot_rl_JumperHard

You may need to add run permissions on the game executable.

bash
chmod +x examples/godot_rl_JumperHard/bin/JumperHard.x86_64

Train and visualize, first download the Stable Baselines 3 example script in the github repo:
bash
python examples/stable_baselines3_example.py --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --experiment_name=Experiment_01 --viz

Deprecated usage with an entrypoint (to be removed in version 1.0):

gdrl --env=gdrl --env_path=examples/godot_rl_JumperHard/bin/JumperHard.x86_64 --experiment_name=Experiment_01 --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 (.NET version) from https://godotengine.org/
2. Open the engine and import the JumperHard example in examples/godot_rl_JumperHard
3. Start in editor training with: python examples/stable_baselines3_example.py

Tutorials

Custom env tutorial

Dedicated tutorial on creating custom environments. We recommend following this tutorial before trying to create your own environment.

Simple env tutorial

Video and textual tutorial on how to create an environment using new sensors (position and reward sensor). Recommended to take the above tutorial first, but you can start with this o

……（后略，见原文）