RLlib is an open-source library for reinforcement learning (RL), offering support for production-level, highly distributed RL workloads while maintaining unified and simple APIs for a large variety of industry applications. Whether you would like to train your agents in a multi-agent setup, purely from offline (historic) datasets, or using externally connected simulators, RLlib offers a simple solution for each of your decision making needs.
The updated Rllib example script allows training environments with single and multiple different policies. To use the new example, installation process is a bit different, you can find it described in the training multiple policies guide.
Below is the older usage process, please refer to the previous section for recommended usage.
If you want to train with rllib, create a new environment e.g.: python -m venv venv.rllib as rllib's dependencies can conflict with those of sb3 and other libraries.
Due to a version clash with gymnasium, stable-baselines3 must be uninstalled before installing rllib.
pip install godot-rl
# remove sb3 and gymnasium installations
pip uninstall -y stable-baselines3 gymnasium
# install rllib
pip install ray[rllib]Usage instructions for envs BallChase, FlyBy and JumperHard.
• Download the env:
gdrl.env_from_hub -r edbeeching/godot_rl_<ENV_NAME>
chmod +x examples/godot_rl_<ENV_NAME>/bin/<ENV_NAME>.x86_64 # linux example• Train a model from scratch:
gdrl --trainer=rllib --env=gdrl --env_path=examples/godot_rl_<ENV_NAME>/bin/<ENV_NAME>.x86_64 --speedup=8 --experiment_name=Experiment_01 --viz
By default rllib will use the hyperparameters in the ppo_test.yaml file on the github repo. You can either modify this file, or create your own one.
Rllib contains many features and RL algorithms, it can be used to create highly complex agent behaviors. We recommend taking the time to read their docs to learn more.