RLtools

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

GrowingOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Stable

License

Open Source

Data freshness

Aging · Jun 8, 2026

Overview

What is RLtools?

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.

Key differentiator

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.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Header-only C++ implementation for easy integrationmedium

Dependency-free design to avoid conflicts with existing projectsmedium

High-performance reinforcement learning algorithms optimized for continuous control tasksmedium

Available Python bindings for broader accessibilitymedium

MIT licensed, open-source projectmedium

↓ Weaknesses

Steep learning curve for non-C++ developershigh

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

Limited Python integration maturitymedium

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

Frequent breaking changes between versionshigh

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.

Small community and limited third-party supportmedium

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.

Debugging can be challenging due to pure C++ implementationmedium

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.

Fit analysis

Who is it for?

✓ Best 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.

✕ Not a fit for

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.

Cost structure

Pricing

Free Tier

Available

Open source — free to use

Starts at

$0

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with RLtools

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →