This example illustrates one of the core challenges of MARL. Imagine a team of chefs that can cook meals very quickly: they must deliberately slow down if the server isn’t able to keep up, lest the food is served cold. While each member of the staff needs to learn to do their particular job as well as possible (chopping vegetables, managing the queue, taking customer orders, etc.), they must also learn how their decisions will affect the other staff members as well. In order for the morning to go smoothly, there must be coordination between the servers, hosts, chefs, and assistants. Picture a busy morning at a popular brunch spot that recently opened. Instead of a single “brain” collecting and coordinating all information from all actors, each agent must be equipped with the ability to reason and act on its own. This means that in MARL settings, while different agents may act in a shared environment, there are restrictions on the ability of agents to share information, observe the world around them, and take actions accordingly. The key theme of MARL is decentralization. This domain is usually referred to as Multi-Agent Reinforcement Learning or MARL. However, one exciting new area of research - where current approaches fail - considers the situation where several agents must learn to work together in order to solve a challenging problem.
Machine learning algorithms are becoming increasingly good at solving all kinds of challenges, including complex, competitive computer games like StarCraft II. In particular, researchers from University of Oxford just released a new test suite that uses the flexibility of the StarCraft II platform in order to challenge other scientists to develop agents that can learn to collaborate, coordinate, and cooperate. While this may seem to imply that there is little room left for developing learning algorithms in the StarCraft II environment, the research community is only just beginning to employ this platform for AI development.
This time, the new AlphaStar algorithm was able to defeat a professional in the popular competitive strategy game StarCraft II. If you follow science news, you’ve probably heard about the latest machine-over-man triumph by DeepMind. Smith, Mikayel Samvelyan, Tabish Rashid, University of OxfordĮditors note: The story below is a guest post written by current and former postgraduate students at the University of Oxford, a member of the NVIDIA AI Labs (NVAIL) program.
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