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
During July 2025, J.D. Selvaraaj developed a graph-based observation and GNN integration extension for the instadeepai/Mava framework, enabling richer multi-agent modeling through graph-structured environment representations. He designed and implemented new network components and wrappers using JAX, Flax, and Jraph, ensuring compatibility with existing Mava modules. This work allowed researchers to experiment with relational architectures and advanced reasoning in multi-agent reinforcement learning settings. By updating core networks and utilities to support graph-based data, J.D. established a foundation for improved analysis of agent interactions. The project demonstrated depth in wrapper design and configuration management, addressing complex environment modeling challenges.
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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