PKU-Alignment/omnisafe

Health v2 · maintenance only

Adoption not part of the score

1149 stars · 161 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

OmniSafe is a PyTorch-based infrastructural framework for safe reinforcement learning research, providing a unified modular toolkit and comprehensive benchmark of safe RL algorithms. It abstracts algorithm types behind Adapter and Wrapper designs to support tens of constrained/safe RL algorithms across diverse domains.

Use cases

• benchmark safe reinforcement learning algorithms
• research constrained RL with safety constraints
• develop and compare safe RL algorithms in PyTorch
• train RL agents that minimize unsafe behavior
• evaluate algorithms on Safety-Gymnasium environments
• build new safe RL algorithms on a modular framework

When to choose

• you need a unified, modular framework for safe/constrained RL research
• you want a reliable benchmark suite for safe RL algorithms
• you work in PyTorch and need out-of-box safe RL algorithm implementations

When to avoid

• you only need standard (unconstrained) RL algorithms
• you need production RL deployment rather than research tooling
• you prefer non-PyTorch frameworks like JAX or TensorFlow

Facets

framework · maturity active

machine-learning reinforcement-learning benchmarking simulation reinforcement-learning machine-learning deep-learning artificial-intelligence python windows safe-reinforcement-learning pytorch constrained-rl safety-gymnasium benchmark research-framework gpu linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PKU-Alignment/omnisafe")