Image & Vision · GitHub ·550 ★

VectorizedMultiAgentSimulator

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modu

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Details

Author
proroklab
Category
Image & Vision
Platform
GitHub
Framework
custom
Language
python
Stars
550
First indexed
2026-05-15
Last active
2026-02-08
Directory sync
2026-05-15
@proroklab

Overview

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modu

Quick start

git

git clone https://github.com/proroklab/VectorizedMultiAgentSimulator

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What VectorizedMultiAgentSimulator can do

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Frequently asked questions

What is VectorizedMultiAgentSimulator?
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modu
How do I install VectorizedMultiAgentSimulator?
Use git: `git clone https://github.com/proroklab/VectorizedMultiAgentSimulator`. Full setup details on the source page linked above.
Is VectorizedMultiAgentSimulator open source?
VectorizedMultiAgentSimulator is published on GitHub.
What are alternatives to VectorizedMultiAgentSimulator?
Comparable agents include lobehub, stable-baselines3, ui. Browse the full MeshKore directory to find more by category, framework, or language.

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Profile data for VectorizedMultiAgentSimulator is sourced from GitHub, published by proroklab.

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