Rl-Tools

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rl-tools is an Open Source AI Frameworks & Infra tool that provides a fast and portable deep reinforcement learning library. It supports various algorithms like TD3, PPO, and SAC, and offers Python bindings for easy integration.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is rl-tools?

rl-tools is an open-source deep reinforcement learning library designed for speed and portability, making it ideal for continuous control tasks. It supports a range of popular reinforcement learning algorithms including TD3, PPO, and SAC, with examples provided for various environments like Pendulum and MuJoCo Ant-v4. The library offers C++ notebooks for documentation and local tinkering via Docker, alongside Python bindings available through PyPI for seamless integration into Python projects. Benchmarks demonstrate its efficiency across different devices and architectures, including macOS and Ubuntu, with specific optimizations for fast training. rl-tools also supports embedded platforms like iOS, Teensy, Crazyflie, and ESP32 for inference and training.

Best used for

Ideal for developers and professors who need to implement and train deep reinforcement learning models, develop custom environments, and benchmark algorithm performance. Especially valuable for those requiring high-speed training and portability across various platforms, including embedded systems.

Common actions

implement reinforcement learning

train AI agents

benchmark RL algorithms

develop custom environments

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Capabilities

Key features

Fast deep reinforcement learning
Multiple RL algorithms (TD3, PPO, SAC)
C++ notebooks for documentation
Python bindings via PyPI
Embedded platform support
Performance benchmarks

Target Audience

developerprofessor

Integrations

dockergymnasium

Pricing & Plans

Open Source

Free

FAQs

What reinforcement learning algorithms does rl-tools support?

rl-tools supports several deep reinforcement learning algorithms, including TD3 (Twin Delayed DDPG), PPO (Proximal Policy Optimization), and SAC (Soft Actor-Critic). Examples for these algorithms are provided for various environments like Pendulum and MuJoCo Ant-v4.

Can I use rl-tools with Python?

Yes, rl-tools provides Python bindings available through PyPI, which can be installed via `pip install rltools`. This allows for easy integration into Python projects, though native RLtools environments may offer faster training compared to Python Gym environments.

Does rl-tools support embedded platforms?

Yes, rl-tools is designed for portability and supports inference and training on various embedded platforms. These include iOS, Teensy, Crazyflie, and ESP32, making it suitable for robotics and other resource-constrained applications.

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Research & Education › Academic Research Coding & Development › Open Source & Models Coding & Development › Testing & QA

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