instadeepai/Mava
Jul 2025 – Jul 2025
1 Month active
Languages Used
JAXPython
Technical Skills
Configuration ManagementFlaxGraph Neural NetworksJAXJraphMulti-Agent Reinforcement Learning
Joseph Daniel Selvaraaj
PROFILE
Joseph Daniel Selvaraaj
In July 2025, J.D. Selvaraaj developed a graph-based extension for the instadeepai/Mava framework, enabling multi-agent reinforcement learning environments to leverage graph neural networks for richer agent interaction modeling. He integrated GNN torsos and graph-based observation pipelines using JAX, Flax, and Jraph, updating core network components and utilities to support graph-structured data. This work introduced new wrappers and network modules compatible with existing Mava components, allowing researchers to experiment with relational architectures. Selvaraaj’s contributions established a foundation for advanced multi-agent reasoning, demonstrating depth in configuration management and wrapper design while addressing the need for flexible, graph-centric environment representations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,019
Activity Months1
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07 focused on expanding Mava with graph-based observations and GNN integration to enhance multi-agent modeling and environment representations. Delivered a dedicated graph-first extension while maintaining compatibility with existing components, enabling researchers to experiment with relational architectures and graph-structured interactions.
1 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07 focused on expanding Mava with graph-based observations and GNN integration to enhance multi-agent modeling and environment representations. Delivered a dedicated graph-first extension while maintaining compatibility with existing components, enabling researchers to experiment with relational architectures and graph-structured interactions.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JAXPython
Technical Skills
Configuration ManagementFlaxGraph Neural NetworksJAXJraphMulti-Agent Reinforcement LearningWrapper Design
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