CleanRL vs Stable-Baselines3

Side-by-side — pricing, strengths, tradeoffs, and fit.

CleanRL

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

Stable-Baselines3

PyTorch-based reinforcement learning algorithms for deep RL research and applications.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

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

One-liner

PyTorch-based reinforcement learning algorithms for deep RL research and applications.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best 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.

✓ Best for

Researchers looking to implement and test deep RL algorithms with PyTorch
Developers building autonomous systems that require reinforcement learning techniques
Academics who need a reliable framework for teaching and research in reinforcement learning

Not ideal for

✕ Not 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.

✕ Not for

Projects requiring real-time performance critical applications where stability is less important than speed
Teams looking for a cloud-based managed service for reinforcement learning

Strengths

↑ Strengths

Single-file implementations for clarity and ease of use.
High-quality code with research-friendly features.
Supports multiple popular Deep Reinforcement Learning algorithms.

↑ Strengths

PyTorch-based implementations of reinforcement learning algorithms
Stability and reliability in deep RL research
Extensive documentation and examples for quick start

Weaknesses

↓ Weaknesses

Limited scalability for large-scale production deployments
Small community and limited third-party integrations
Documentation is concise but lacks depth for beginners

↓ Weaknesses

Frequent breaking changes between versions
Limited support for non-PyTorch frameworks
Resource-intensive for large-scale applications
Full CleanRL profile →Full Stable-Baselines3 profile →