Introduction

Training multiple autonomous agents to coordinate in complex environments is often a computational nightmare, plagued by high memory overhead and slow iteration cycles. Mava, a research-friendly codebase for multi-agent reinforcement learning (MARL) with over 900 GitHub stars, solves this by leveraging the high-performance computing capabilities of JAX. By treating MARL as a sequence modeling problem and utilizing hardware accelerators, Mava enables researchers to test hypotheses in minutes rather than hours, drastically reducing the time to discovery in the field of cooperative AI.

What Is Mava?

Mava is a research-focused framework for multi-agent reinforcement learning (MARL) written purely in JAX. It is designed to provide a fast, scalable, and flexible environment for experimenting with MARL algorithms, specifically targeting cooperative multi-agent systems where agents must work together toward a shared goal.

Maintained by InstaDeep, the framework is licensed under the Apache License 2.0. It focuses on simplicity and clarity, following a “code philosophy” inspired by CleanRL and PureJaxRL, meaning it prioritizes single-file implementations over complex modular abstractions to make the code more accessible and easier for researchers to modify.

Why Mava Matters

Multi-agent reinforcement learning is inherently computationally expensive. Traditional frameworks often struggle to scale as the number of agents increases, leading to a “curse of dimensionality” where the state and action spaces explode. Mava addresses this by utilizing JAX’s vmap and pmap for massive parallelization across GPUs and TPUs.

The framework’s primary value proposition is speed. According to its technical reports, Mava can achieve speed improvements of 10-100x compared to other popular MARL frameworks. This allows researchers to perform rigorous statistical testing and run hundreds of experiments in a fraction of the time it would take in PyTorch-based libraries.

Furthermore, Mava introduces the Sable algorithm, a state-of-the-art sequence modeling approach that maintains performance even when scaling to over a thousand agents. This fills a critical gap in the industry: the ability to train large-scale agent teams without linear increases in memory usage.

Key Features

• JAX-Native Implementation: Built entirely in JAX, allowing for end-to-end JIT compilation of the full training loop, which maximizes hardware accelerator utilization.
• Sable Algorithm: A performant sequence model for MARL that adapts the retention mechanism from Retentive Networks to achieve long-context memory for temporal reasoning.
• Massive Scalability: Capable of handling environments with more than 1,000 agents while maintaining linear memory usage, making it ideal for large-scale simulations.
• Anakin and Sebulba Architectures: Offers two distinct distribution architectures. Anakin is used for JAX-native environments for maximum speed, while Sebulba allows JAX-based trainers to interact with non-JAX environments.
• Research-Friendly Codebase: Prioritizes clarity and simplicity over modularity, making it an excellent resource for practitioners to build upon without navigating deep inheritance trees.
• Standardized Evaluation: Natively supports logging to JSON files that adhere to the standard suggested by Gorsane et al. (2022), facilitating easy downstream plotting and aggregation.
• Multi-Agent Transformer (MAT) Support: Includes implementations of the Multi-agent Transformer, which casts cooperative MARL as a sequence modeling problem.

How Mava Compares

Mava is designed for a different primary user than Ray RLlib or PyMARL. While RLlib is a production-grade, distributed system for general RL, Mava is a specialized research tool optimized for the specific patterns of multi-agent systems in JAX.

The primary tradeoff is that Mava is not intended to be a modular library to be imported into other projects. Instead, it is a research tool where you clone the repository and modify the implementations directly. This is a stark contrast to RLlib, which provides a high-level API for configuring agents. For researchers who need to absolute control over the training loop and the most possible speed from JAX, Mava is the superior choice.

Getting Started: Installation

Mava is not designed to be installed as a pip package. Instead, it is recommended to clone the repository and manage dependencies using uv for fast environment setup.

Prerequisites

A JAX-compatible environment (GPU or TPU) is highly recommended for the performance gains mentioned in the documentation.

Installation via uv

git clone https://github.com/instadeepai/Mava.git
cd Mava
uv sync
source .venv/bin/activate

GPU/TPU Aware JAX Installation

To ensure Mava utilizes your hardware accelerators, install the GPU-aware version of JAX:

uv sync --extra cuda12

How to Use Mava

Mava operates on the concept of a “system,” which is a full MARL algorithm specification. A system consists of an executor (which interacts with the environment), a trainer (which updates the policy), and a dataset (which stores generated experience).

To begin, you typically select an implementation from the /mava/systems/ directory. For example, if you are using the Multi-agent Transformer (MAT), you would navigate to the MAT system folder and run the training script. The framework handles the parallelization across agents and environments automatically via JAX’s vmap.

The workflow generally follows this pattern: define your environment wrapper, select your trainer, and execute the training loop. Because Mava follows the CleanRL philosophy, the logic is contained within the script, making it easy to trace the flow of data from observation to action selection.

Code Examples

While Mava’s implementations are script-based rather than API-based, the core logic for agent interaction is handled through JAX transformations. The following example demonstrates the conceptual flow of how Mava handles multi-agent observations in a JAX-native way:

# Conceptual example of Mava's parallel observation processing
import jax
import jax.numpy as jnp

# Observations for N agents across M environments
# Shape: (num_envs, num_agents, obs_dim)
observations = jnp.zeros((10, 5, 64))

# A simple policy function
def policy(obs):n    return jax.random.uniform(obs.shape[0], (1,))

# Use vmap to parallelize the policy across agents and environments
# This is what Mava does under the hood to achieve its speed
parallel_policy = jax.vmap(jax.vmap(policy))
actions = parallel_policy(observations)

For actual implementation scripts, researchers should refer to the /mava/systems/ directory in the repository, where full implementations of Sable, MAT, and Q-Learning are provided as standalone scripts.

Real-World Use Cases

Mava is specifically designed for cooperative MARL, where a team of agents must coordinate to solve a task. This is ideal for the following scenarios:

• Robot Warehouse Management: Coordinating a fleet of hundreds of robots to move items without colliding, a scenario where Mava’s Sable algorithm shines due to its scalability to 1,000+ agents.
• Autonomous Traffic Control: Training a network of traffic lights or autonomous vehicles to minimize congestion in a city-wide simulation.
• Distributed Resource Management: Managing electricity grids or inventory control in critical distribution networks where decentralized coordination is required.
• MARL Algorithm Research: Developing and testing new sequence modeling approaches to MARL, using Mava as a high-performance baseline for benchmarking against other frameworks like EPyMARL.

Contributing to Mava

Mava welcomes contributions from the research community. Because the project follows a research-first approach, contributions should focus on adding new algorithm implementations or improving the environment wrappers.

To contribute, you must sign the Contributor License Agreement (CLA) and follow the project’s community guidelines. Bug reports and feature requests should be submitted via GitHub issues. Pull requests should adhere to the project’s naming conventions for branches and commits to maintain the clarity of the research codebase.

Community and Support

Mava is maintained by InstaDeep, an AI research company. Support is primarily handled through GitHub Discussions and the official repository’s issue tracker. Since Mava is a research tool rather than a commercial product, the primary source of truth is the code itself and the accompanying technical papers (such as the Sable paper).

The community consists largely of MARL researchers and graduate students. You can find detailed experimental data and hyperparameters for the Sable algorithm on the official Sable Google Site.

Conclusion

Mava is the right choice for researchers and engineers who need the absolute maximum performance from their multi-agent reinforcement learning experiments. By moving away from the modular abstractions of traditional libraries and embracing the JAX ecosystem, Mava provides a speed and scalability advantage that is critical for testing complex cooperative AI behaviors.

While it may not be the suitable tool for a production deployment where a high-level API is needed, it is an unrivaled tool for rapid prototyping andL research. If you are working with cooperative agents and need to scale to hundreds or thousands of agents, Mava’s Sable algorithm and JAX-native architecture are the most efficient path to discovery.

Star the repo, try the quickstart, and join the MARL research community.

Resources

• Mava GitHub Repository
• Sable MARL Official Site
• Mava Technical Report (arXiv)