Empirically Evaluating Multiagent Learning Algorithms

By Erik Zawadzki

An important part of real-world environments is that they are home to multiple parties, each with their own goals and agendas. These other agents make learning in these environments distinct from learning in single agent settings: for instance, while your algorithm is learning, it is simultaneously also teaching.

Research into multiagent learning has attracted a lot of focus recently. This attention has created a wealth of new algorithms, but a lack of general understanding of how these algorithms perform empirically. I will describe an experiment, unprecedented in terms of scale, which will cast some light on how these algorithms perform. Additionally, I will talk about the unique analytical challenges posed by multiagent learning algorithms and the methods that were used to analyze their performance.

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