Home Projects Mava
Mava
Python

Mava

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

by instadeepai · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
jaxmarlmulti-agent-reinforcement-learningApache-2.0Python
View on GitHub
In plain words

Quickly test and develop new ideas in multi-agent AI systems with a user-friendly codebase.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
Mava — GitHub preview card
📈 Star history
924923
2026-07-202026-08-31
📈 Track Mava

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Mava has 923 stars on GitHub. It has been forked 122 times. Mava is written mainly in Python. It has been in active development since 2021. Mava is available under the Apache-2.0 license. Its main topics are jax, marl, multi-agent-reinforcement-learning, multi-agent-systems.

Frequently asked questions

What is Mava?

🦁 A research-friendly codebase for fast experimentation of multi-agent reinforcement learning in JAX

Is Mava open source?

Mava is an open-source project. It is released under the Apache-2.0 license.

Is Mava free?

Yes. Mava is free and open source — you can use, modify and self-host it.

What license does Mava use?

Mava is available under the Apache-2.0 license.

What language is Mava written in?

Mava is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — Mava

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=instadeepai-mava)](https://olud.ai/project/instadeepai-mava.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.