Toni-SM/skrl

Health v2 · maintenance only

Adoption not part of the score

1098 stars · 157 forks observed · 2026-10-01

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

skrl is an open-source modular Reinforcement Learning library written in Python, implemented in PyTorch, JAX, and NVIDIA Warp. It supports Gymnasium/Gym, PettingZoo, NVIDIA Isaac Lab, MuJoCo Playground, and other environments, including multi-agent and simultaneous multi-environment training.

Use cases

• train reinforcement learning agents in PyTorch or JAX
• train RL agents on NVIDIA Isaac Lab or MuJoCo Playground environments
• run multi-agent reinforcement learning experiments
• train agents across multiple environments simultaneously in one run
• learn and prototype RL algorithms with readable modular code
• train RL policies for robotics simulation

When to choose

• you need a modular, readable RL library with multi-backend support (PyTorch, JAX, Warp)
• you work with Isaac Lab, MuJoCo, Gymnasium, or PettingZoo environments
• you need multi-agent RL or scoped multi-environment training
• you want an MIT-licensed library with active development and docs

When to avoid

• you need a highly optimized single-algorithm implementation with maximum throughput
• you require backends outside PyTorch/JAX/Warp
• you need non-RL deep learning tooling like supervised training pipelines

Facets

library · maturity active

reinforcement-learning machine-learning deep-learning simulation reinforcement-learning machine-learning robotics deep-learning python cross-platform pytorch jax nvidia-warp gymnasium isaac-lab mujoco multi-agent robotics gpu

Member repositories

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markdown · JSON · MCP: product_card(name="Toni-SM/skrl")