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

🚀 A fast safe reinforcement learning library in PyTorch

🤖 Elegant implementations of offline safe RL algorithms 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://www.roboticsproceedings.org/rss20/p104.html

[AAAI 2024 (Oral)] Safety-MuJoCo Environments.

Safety-gated TH06 learning agent with corpus benchmarks, conservative offline RL, and Linux headless simulation

Composable safety-filter and auditability layer for tensor-parallel reinforcement learning. CBF safety filters, watchdog registries, pre-registration artifacts, failure forensics.

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

SAFIRL: Shielded RL with CBF/MPC on Franka-MuJoCo - reproducible training/evaluation, STL-based verification, benchmarks, Docker/CI.

Safe Reinforcement Learning for Drone Control using Control Barrier Functions

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

Official implementation of C-TRPO

Poster about Curriculum Induction for Safe Reinforcement Learning

SafeSABR: risk-calibrated adaptive bitrate streaming over Starlink networks

Simulation-based safe reinforcement learning framework for energy-efficient distillation control using Aspen Plus, surrogate modeling and FOPDT dynamics.

[ECAI-2025] SPOWL: A JAX-based Safe RL framework that adaptively combines planning and policy learning with dynamic safety thresholds.