JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

🤖 Elegant implementations of offline safe RL algorithms in PyTorch

🚀 A fast safe reinforcement learning library in PyTorch

🔥 Datasets and env wrappers for offline safe reinforcement learning

PyTorch implementation of Constrained Reinforcement Learning for Soft Actor Critic Algorithm

A Survey Analyzing Generalization in Deep Reinforcement Learning

A Multiplicative Value Function for Safe and Efficient Reinforcement Learning. IROS 2023.

Official Code Repository for the POLICEd-RL Paper: https://arxiv.org/abs/2403.13297

Safety-MuJoCo Environments

Correct-by-synthesis reinforcement learning with temporal logic constraints (CoRL)

Poster about Curriculum Induction for Safe Reinforcement Learning

Author implementation of DSUP(q) algorithms from the NeurIPS 2024 paper "Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning"

Blog Post about Curriculum Induction for Safe Reinforcement Learning