Model Hubs & Serving

CleanRL

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

Status
Generally available
Last checked
Checked · Oct 6, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

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.

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.

When it fits — and when it doesn’t

A good fit 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.

Look elsewhere if

  • 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.

Known weaknesses

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

  1. 01

    Limited scalability for large-scale production deployments

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

  2. 02

    Small community and limited third-party integrations

    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.

  3. 03

    Documentation is concise but lacks depth for beginners

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

What it does well

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

  • High-quality code with research-friendly features.

  • Supports multiple popular Deep Reinforcement Learning algorithms.

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

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Set up CleanRL

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

Open the setup guide →