---
id: skrl
version: "2.1.0"
license: MIT License
license_treatment: permissive
maintenance: active
---

# skrl — Modular and flexible library for reinforcement learning on PyTorch and JAX

License: permissive · Maintenance: active · Downloads: 142.3K/mo

## What it is and what it does

skrl is a modular reinforcement learning library designed for clarity and transparency in algorithm implementation. It abstracts away boilerplate by providing reusable agent and environment components that work with PyTorch, JAX, or NVIDIA Warp backends, letting you focus on algorithm logic rather than framework plumbing. The library supports multiple environment interfaces—Gymnasium, PettingZoo, ManiSkill, Isaac Lab, and MuJoCo—and enables training multiple agent scopes (subsets of environments) in a single run, with optional resource sharing between scopes.

Typical use involves wrapping your environment, instantiating an agent with a chosen algorithm (e.g., PPO, DQN), and running training loops. The modular design means you can swap algorithms, environments, and backends with minimal code changes. It is actively maintained and documented, with a published research paper backing its design philosophy.

Use it for:

- Train RL agents on Gymnasium environments (CartPole, Atari, MuJoCo) with a clean, algorithm-agnostic API.
- Develop and test new RL algorithms in a transparent, readable codebase without reimplementing environment boilerplate.
- Run multi-agent or multi-environment training in Isaac Lab or MuJoCo simulations with scope-based resource management.
- Prototype RL solutions using PyTorch, then port to JAX or NVIDIA Warp for performance without rewriting agent logic.
- Conduct research on algorithm variants with modular components for policies, value functions, and memory buffers.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

skrl is a modular reinforcement learning library that implements algorithms in PyTorch, JAX, and NVIDIA Warp, supporting Gymnasium, PettingZoo, ManiSkill, Isaac Lab, and MuJoCo environments with multi-scope agent training.

Yes, if you are developing or researching reinforcement learning algorithms. The modular design, support for multiple backends, and active maintenance make it a solid choice for RL projects. Install it if you want readable, transparent algorithm implementations and multi-environment training capabilities. Not necessary if you only need a simple single-algorithm solver or prefer a more opinionated framework.

## Install

pip install skrl
uv add skrl
poetry add skrl

## Installing skrl

Before you install: Low install friction with a pure-Python wheel distribution. Active maintenance with recent commits (last commit 2026-05-11) and steady releases since 2022-07-11. Requires Python >=3.10, which is current but not legacy-compatible.

License in practice: MIT License (permissive) allows commercial and private use with minimal restrictions, making it suitable for research, production, and derivative work.

Quickstart:

pip install skrl

import gymnasium as gym
from skrl.agents.torch import PPO
from skrl.environments.torch import GymWrapper

env = GymWrapper(gym.make('CartPole-v1'))
agent = PPO(env.observation_space, env.action_space)

Requires Python >=3.10. Actual training requires PyTorch or JAX installed separately; skrl does not declare them as hard dependencies.

Verify before relying:

- Whether PyTorch or JAX must be pre-installed or if skrl can guide users to install them.
- Performance characteristics and scalability limits for multi-scope training across many environments.
- Completeness of algorithm coverage compared to other RL libraries in the ecosystem.

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 142.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

reinforcement learning library, RL algorithm implementation, gymnasium environment training, multi-agent RL framework, PyTorch JAX reinforcement learning, modular RL agents, Isaac Lab training, reinforcement-learning, multi-backend, modular-agents

[View on SkillFed](https://skillfed.io/packages/skrl) · [View on PyPI](https://pypi.org/project/skrl/)