  Multi-agent reinforcement learning (MARL) research is inherently
computationally expensive and it is often difficult to obtain a sufficient
number of experiment samples to test hypotheses and make robust statistical
claims. Furthermore, MARL algorithms are typically complex in their design and
can be tricky to implement correctly. These aspects of MARL present a difficult
challenge when it comes to creating useful software for advanced research. Our
criteria for such software is that it should be simple enough to use to
implement new ideas quickly, while at the same time be scalable and fast enough
to test those ideas in a reasonable amount of time. In this preliminary
technical report, we introduce Mava, a research library for MARL written purely
in JAX, that aims to fulfill these criteria. We discuss the design and core
features of Mava, and demonstrate its use and performance across a variety of
environments. In particular, we show Mava's substantial speed advantage, with
improvements of 10-100x compared to other popular MARL frameworks, while
maintaining strong performance. This allows for researchers to test ideas in a
few minutes instead of several hours. Finally, Mava forms part of an ecosystem
of libraries that seamlessly integrate with each other to help facilitate
advanced research in MARL. We hope Mava will benefit the community and help
drive scientifically sound and statistically robust research in the field. The
open-source repository for Mava is available at
https://github.com/instadeepai/Mava.
