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RLtools

Fastest deep reinforcement learning library for continuous control in C++ with Python bindings.

Status
Generally available
Last checked
Aging · Sep 3, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

RLtools is a high-performance deep reinforcement learning library designed for continuous control tasks, implemented in pure C++ without dependencies. It also offers Python bindings to facilitate integration into existing workflows.

RLtools stands out as the fastest deep reinforcement learning library for continuous control, implemented in pure C++ without dependencies, making it ideal for high-performance applications and rapid prototyping.

When it fits — and when it doesn’t

A good fit for

  • Developers working on high-performance reinforcement learning tasks in C++ who need a dependency-free solution.

  • Researchers requiring a lightweight, header-only library for rapid prototyping of reinforcement learning algorithms.

Look elsewhere if

  • Projects that require extensive integration with other libraries and frameworks due to RLtools' dependency-free design.

  • Developers preferring a more comprehensive ecosystem with built-in support for various machine learning tasks beyond reinforcement learning.

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Steep learning curve for non-C++ developers

    The library is implemented in C++, which may be challenging for developers without a strong background in this language.

  2. 02

    Limited Python integration maturity

    While Python bindings are provided, the integration and documentation for these bindings can be less mature compared to the core C++ library.

  3. 03

    Frequent breaking changes between versions

    Historical version updates have included significant API changes that required substantial refactoring of user code, such as the migration from v0.1 to v0.2.

  4. 04

    Small community and limited third-party support

    As an open-source project with a niche focus on C++ reinforcement learning, it has a relatively small user base and fewer contributions or integrations compared to larger frameworks like TensorFlow or PyTorch.

  5. 05

    Debugging can be challenging due to pure C++ implementation

    The lack of dependencies while beneficial for performance, also means developers must rely on lower-level debugging techniques and tools available in the C++ ecosystem.

What it does well

  • Header-only C++ implementation for easy integration

  • Dependency-free design to avoid conflicts with existing projects

  • High-performance reinforcement learning algorithms optimized for continuous control tasks

  • Available Python bindings for broader accessibility

  • MIT licensed, open-source project

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up RLtools

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →