Mava
by instadeepai
Research-first MARL library in JAX for fast experiments and reproducible results
Overview
Provides a research-focused codebase for building and experimenting with multi-agent reinforcement learning (MARL) in JAX, following the Hierarchical Multi-Agent Pattern. Organizes algorithms, training loops, and utilities to make rapid iteration and reproducible research straightforward. Includes Agent Registry Pattern components and example scenarios that let you swap policies, environments, and learning rules with minimal boilerplate.
Key Benefits
When to Use
Researchers and engineers who need a modular, JAX-native framework to prototype and evaluate multi-agent RL algorithms and interaction dynamics. This makes it ideal for teams adopting Agentic RAG Pattern for reliability-aware tooling.
Applications
- Run controlled MARL experiments to observe emergent agent interactions and failure modes
- Compare policy variants and training regimes to build agent track records for later evaluation
- Prototype multi-agent delegation and coordination strategies before integrating into agent-to-agent evaluation pipelines