Skrl

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skrl is an AI Agents & Automation tool that provides a modular Reinforcement Learning library. It supports multiple deep learning frameworks and various environments for research and development.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is skrl?

skrl is an open-source, modular Reinforcement Learning (RL) library implemented in Python, supporting PyTorch, JAX, and NVIDIA Warp. It is designed with a focus on modularity, readability, simplicity, and transparency of algorithm implementation, making it suitable for both research and development. The library supports a wide range of environment interfaces, including OpenAI Gym, Farama Gymnasium, PettingZoo, and ManiSkill. Additionally, it allows for loading and configuring NVIDIA Isaac Lab and MuJoCo Playground environments, enabling simultaneous training of agents by scopes within the same run. skrl is under active continuous development, with the latest updates available on its develop branch.

Best used for

Ideal for researchers and developers who need to implement and experiment with Reinforcement Learning algorithms, train AI agents in diverse simulated environments, and conduct robotics research. Especially valuable for those requiring flexibility across deep learning frameworks like PyTorch, JAX, and NVIDIA Warp.

Common actions

implement RL algorithms

train AI agents

conduct robotics research

develop machine learning models

collaborationdeepfakeautomated workflowlow-code/no-codeopen-sourceworkflowsface swappinggithub copilot"AI Agents"

Capabilities

Key features

Modular RL library
PyTorch, JAX, NVIDIA Warp
Gymnasium/Gym support
Isaac Lab/MuJoCo integration
Simultaneous agent training

Target Audience

professorresearcherdeveloper

Integrations

pytorchjaxnvidia-warpopenai-gymfarama-gymnasiumpettingzoomaniskillnvidia-isaac-lab+ 1 more

Pricing & Plans

Open Source

Free

FAQs

What deep learning frameworks does skrl support?

skrl is designed to be flexible and supports multiple deep learning frameworks. Specifically, it is implemented in Python and provides compatibility with PyTorch, JAX, and NVIDIA Warp, allowing users to leverage their preferred framework for Reinforcement Learning tasks.

Can skrl be used with various simulation environments?

Yes, skrl offers broad compatibility with different simulation environments. It supports popular interfaces like OpenAI Gym, Farama Gymnasium, and PettingZoo. Additionally, it integrates with NVIDIA Isaac Lab and MuJoCo Playground, enabling diverse experimental setups for agent training.

Is skrl suitable for multi-agent training scenarios?

skrl allows for the simultaneous training of agents by scopes, which are subsets of environments. These scopes may or may not share resources within the same run, making it suitable for complex multi-agent training scenarios and efficient resource utilization.

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Also listed in

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Research & Education › Academic Research Coding & Development › Game Development Coding & Development › Open Source & Models Research & Education › Scientific Computing

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