# Advanced usage with rllib

[RLlib](https://docs.ray.io/en/latest/rllib/index.html) 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.

## Usage with Rllib example (Recommended)

The updated [Rllib example](https://github.com/edbeeching/godot_rl_agents/blob/main/examples/rllib_example.py) 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](https://github.com/edbeeching/godot_rl_agents/blob/main/docs/TRAINING_MULTIPLE_POLICIES.md) guide.

## Installation
**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.
```bash
pip install godot-rl
# remove sb3 and gymnasium installations
pip uninstall -y stable-baselines3 gymnasium
# install rllib
pip install ray[rllib]
```

## Basic Environment Usage
Usage instructions for envs **BallChase**, **FlyBy** and **JumperHard.**

• Download the env:

```bash
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](https://docs.ray.io/en/latest/rllib/index.html) to learn more.