v0.1.0

First Release

DSRL (Datasets for Safe Reinforcement Learning) provides a rich collection of datasets specifically designed for offline Safe Reinforcement Learning (RL). Created with the objective of fostering progress in offline safe RL research, DSRL bridges a crucial gap in the availability of safety-centric public benchmarks and datasets.

DSRL provides:

1. Diverse datasets: 38 datasets across different safe RL environments and difficulty levels in SafetyGymnasium, BulletSafetyGym, and MetaDrive, all prepared with safety considerations.
2. Consistent API with D4RL: For easy use and evaluation of offline learning methods.
3. Data post-processing filters: Allowing alteration of data density, noise level, and reward distributions to simulate various data collection conditions.

This package is a part of a comprehensive benchmarking suite that includes FSRL and OSRL and aims to promote advancements in the development and evaluation of safe learning algorithms.

To learn more, please visit our project website.