skrl
Active·★ 1.1k·Updated 2026-05-11
★ Trending★ Essential
skrl is an open-source, modular Reinforcement Learning library implemented in Python, supporting PyTorch, JAX, and NVIDIA Warp. It focuses on modularity, readability, simplicity, and transparent algorithm implementation, also supporting various environment interfaces like Gym, Gymnasium, and Isaac Lab.
skrl is currently grouped under RAG / Knowledge Base, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward Modular and extensible design and Developing and testing new Reinforcement Learning algorithms. It also shows measurable community traction with 1.1k GitHub stars.
#Reinforcement Learning#Python#PyTorch#JAX#NVIDIA Warp#Coding
01
Features
01Modular and extensible design
02Transparent algorithm implementation
03Multi-framework support (PyTorch, JAX, NVIDIA Warp)
04Compatibility with various environment interfaces (Gym, Gymnasium, PettingZoo, ManiSkill)
05Simultaneous training in NVIDIA Isaac Lab and MuJoCo Playground with scope-based resource sharing
02
Why choose it
+Modular and extensible design
+Developing and testing new Reinforcement Learning algorithms
+Covers 9 supported environments or platforms, which is helpful for broader deployment needs.
+The latest recorded update is 2026-05-11, which suggests the project is still actively maintained.
03
Trade-offs
!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.
04
Compatibility
PyTorch
Native
Verified via docs
JAX
Native
Verified via docs
NVIDIA Warp
Native
Verified via docs
OpenAI Gym
Supported
Verified via docs
Farama Gymnasium
Supported
Verified via docs
Farama PettingZoo
Supported
Verified via docs
05
Quick start
1
$ pip install skrl
06
Use cases
↳Developing and testing new Reinforcement Learning algorithms
↳Training AI agents in various simulated environments (e.g., robotic control, game AI)
↳Research in Reinforcement Learning leveraging multiple backend frameworks
07
How it compares
≈skrl sits in the RAG / Knowledge Base category, so it makes more sense to evaluate it alongside tools like mindsdb instead of in isolation.
≈If your main need is closer to "Developing and testing new Reinforcement Learning algorithms", that use case is a better lens for comparison than broad feature checklists alone.
≈skrl's licensing and community traction are both easier to judge in category context.
08
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