ray-utilities 0.3.1

Utilities for Ray RLlib and related workflows. Includes experiment setup, callbacks, and JAX model utilities.

Ray Utilities

Features

Many Features are stand-alone and can be used independently. The main features include:

• JAX PPO for RLlib: A JAX-based implementation of the Proximal Policy Optimization (PPO) algorithm compatible with RLlib Algorithm.
• Ray + Optuna Grid Search + Optuna Pruners: Extends ray's OptunaSearch to be compatible with
• Experiment Framework: A base class for setting up experiments with dynamic parameters and parameter spaces, easily run via CLI and ray.tune.Tuner.
• Reproducible Environments: Reproducible environments for experiments using ray.tune by using a more sophisticated seeding mechanism.
• Dynamic Parameter Tuning (WIP): Support for dynamic tuning of parameters during experiments. ray.tune.grid_search and Optuna pruners can work as a Stopper.

Furthermore smaller features:

• Exact Environment Step Sampling: RLlib sampling is slightly off and contains masked samples. A callback and connector piece correct this.
• Improved Logger Callbacks: Improved csv, Tensorboard, Wandb, Comet logger callbacks. Cleaner logs and better video handling.
• PPO Torch Learner with Gradient Accumulation: A PPO learner that supports gradient accumulation, useful for training with large batch sizes.

Installation

Install from PyPI

pip install ray_utilities

Install latest version

Clone the repository and install the package using pip:

git clone https://github.com/Daraan/ray_utilities.git
cd ray_utilities
pip install .

Documentation (Work in Progress)

Hosted on Read the Docs: https://ray-utilities.readthedocs.io/

Run Experiments via CLI

Simple entry point:

# File: run_experiment.py
from ray_utilities import run_tune
from ray_utilities.setup import PPOSetup

if __name__ == "__main__":
    with PPOSetup() as setup:  # Replace with your own setup class
        # The setup takes care of many settings passed in via the CLI
        # but the config (an rllib.AlgorithmConfig) can be adjusted
        # inside the code as well.
        # Changes made in this with block are tracked for checkpoint reloads
        setup.config.training(num_epochs=10)
    results = run_tune(setup)

Run the experiment:

python run_experiment.py -a MLP

[!NOTE]
It is recommended to subclass AlgorithmSetup or ExperimentSetupBase to define your own setup. Extend DefaultArgumentParser to add custom CLI arguments. Above's PPOSetup is a very minimalistic example.