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Coordinated Multi-Agent Imitation Learning

Le, Hoang M. and Yue, Yisong and Carr, Peter and Lucey, Patrick (2017) Coordinated Multi-Agent Imitation Learning. Proceedings of Machine Learning Research, 70 . pp. 1995-2003. ISSN 1938-7228.

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We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.

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Yue, Yisong0000-0001-9127-1989
Additional Information:© 2017 the authors. This work was funded in part by NSF Awards #1564330 & #1637598, JPL PDF IAMS100224, a Bloomberg Data Science Research Grant, and a gift from Northrop Grumman.
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Bloomberg Data ScienceUNSPECIFIED
Northrop GrummanUNSPECIFIED
Record Number:CaltechAUTHORS:20170829-143913716
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:80920
Deposited By: Tony Diaz
Deposited On:30 Aug 2017 17:12
Last Modified:09 Mar 2020 13:19

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