CleanRL

High-quality single file implementations of Deep Reinforcement Learning algorithms.

EstablishedOpen SourceLow lock-in
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Pricing

Free tier

Flat rate

Adoption

→Stable

License

Open Source

Data freshness

Verified · Jul 12, 2026

Overview

What is CleanRL?

CleanRL offers high-quality, single-file implementations of popular Deep Reinforcement Learning algorithms like PPO, DQN, C51, DDPG, TD3, SAC, and PPG. It is designed to be research-friendly with a focus on clarity and ease of use.

Key differentiator

“CleanRL stands out by providing high-quality, single-file implementations of popular Deep Reinforcement Learning algorithms with a focus on clarity and ease of use, making it ideal for research and educational purposes.”

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Single-file implementations for clarity and ease of use.medium

High-quality code with research-friendly features.medium

Supports multiple popular Deep Reinforcement Learning algorithms.medium

↓ Weaknesses

Limited scalability for large-scale production deploymentshigh

The single-file approach and focus on research clarity may not be optimized for performance at scale.

Small community and limited third-party integrationsmedium

Being a relatively new project, CleanRL has a smaller user base and fewer contributed plugins or extensions compared to more established frameworks like Stable Baselines3.

Documentation is concise but lacks depth for beginnershigh

While the code is clear and well-documented, there may not be enough explanatory documentation or tutorials for those new to Deep Reinforcement Learning.

Fit analysis

Who is it for?

✓ Best for

Researchers who need high-quality and easy-to-understand implementations for testing new ideas in reinforcement learning.

Educators looking to provide students with clear examples of popular RL algorithms.

Developers working on projects that require a deep understanding of the underlying mechanics of reinforcement learning.

✕ Not a fit for

Projects requiring real-time performance or low-latency execution, as CleanRL focuses more on clarity and research than optimization for speed.

Teams looking for a fully managed service or platform to deploy RL models in production environments.

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

Alternatives

Stable-Baselines3

Next step

Get Started with CleanRL

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

View Setup Guide →