Improved Structural Discovery and Representation Learning of Multi-Agent Data

• URL: http://arxiv.org/abs/1912.13107v1
• Date: Mon, 30 Dec 2019 22:49:55 GMT
• Title: Improved Structural Discovery and Representation Learning of Multi-Agent Data
• Authors: Jennifer Hobbs, Matthew Holbrook, Nathan Frank, Long Sha, Patrick Lucey
• Abstract summary: We present a dynamic alignment method which provides a robust ordering of structured multi-agent data. We demonstrate the value of this approach using a large amount of soccer tracking data from a professional league.
• Score: 5.40729975786985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Central to all machine learning algorithms is data representation. For multi-agent systems, selecting a representation which adequately captures the interactions among agents is challenging due to the latent group structure which tends to vary depending on context. However, in multi-agent systems with strong group structure, we can simultaneously learn this structure and map a set of agents to a consistently ordered representation for further learning. In this paper, we present a dynamic alignment method which provides a robust ordering of structured multi-agent data enabling representation learning to occur in a fraction of the time of previous methods. We demonstrate the value of this approach using a large amount of soccer tracking data from a professional league.

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