ADVERTORIAL
WISDOM AND TEAMWORK OPEN-SOURCED
Mava’s the Made-inAfrica Multi-Agent Reinforcement Learning Framework Many of humanity’s greatest achievements arose from our ability to work together. The complex and distributed problems the world collectively faces now call for a new wave of sophisticated AI cooperation strategies. Responding to that call, an all-Africa-based team at InstaDeep created Mava.
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If you want to go quickly, go alone, an African proverb counsels. If you want to go far, go together
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ou can hear the wisdom of generations about the value of teamwork reverberating through these words. As we face challenges such as managing scarce resources under pressure due to climate change, ensuring critical supply routes keep flowing or enlist robots for remote rescue and exploration missions, weaving teamwork strategies into AI tools is crucial. That’s why InstaDeep created Mava: a research framework specifically designed for
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building scalable, high-quality Multi-Agent Reinforcement Learning (MARL) systems. Mava provides components, abstractions, utilities, and tools for MARL. It can easily scale with multi-process system training and execution while providing a high level of flexibility and new creative possibilities. “At InstaDeep, we have a real passion for innovation, consistent with our mission to build an AI-first world that benefits everyone,” said Karim Beguir, InstaDeep’s CEO and Co-Founder. Several frameworks have emerged in the field of single-agent reinforcement learning (RL), including Dopamine, RLlib , and Acme to name just a few. These aim to help the AI community build effective and scalable agents. However, a limitation of these existing frameworks is that very few focus exclusively on MARL – an increasingly active research field with its own set of challenges and opportunities. InstaDeep aims to fill this gap with Mava.
By focusing on MARL, Mava leverages the natural structure of Multi-Agent problems. This ensures Mava remains lightweight and flexible while at the same time providing tailored support for MARL. InstaDeep’s decision to opensource Mava stems from its passion for contributing to the development of MARL, supporting open collaboration, and a commitment to helping develop the wider community, especially across Africa. InstaDeep itself has also benefited from open-source software and wants to give back. “We’re proud to open-source Mava, a world-class framework entirely designed and built by an all-African, all-star team of InstaDeepers,” Beguir said. Mava is the latest in a flurry of 2021 open-source releases by InstaDeep, including three massive bio data repositories as part of DeepChain Apps in May and a natural language processing (NLP) model for the Tunisian dialect, an under-resourced African language, in June. “Working on Mava has been a wonderful experience and a true team effort in collaboration with our African offices in South Africa, Nigeria and Tunisia,” said Arnu Pretorius, the InstaDeep AI Research Scientist who leads the team in Cape Town. “It really showcases the talent we have on the continent. Not only have we begun to enter the conversation of AI,” Pretorius said, adding, “but we are now starting to take ownership of key technologies, helping to shape the future and contributing to making the world a better place using AI.”
Why MARL? In Xhosa, one of South Africa’s eleven official languages, “Mava” means experience or wisdom. Only by working together, has humanity been able to accomplish some of its greatest achievements. This has never been more true. The problems we face are distributed, complex and difficult to solve and often require sophisticated strategies of cooperation for us to make any progress. From the standpoint of using AI for problemsolving, this drives us to harness and develop useful computational frameworks for decision-making and cooperation. One such framework is MARL. MARL extends the decision-making capabilities of single-agent RL to the setting of distributed decision-making problems. In MARL, multiple agents are trained to
