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Generative Multi-Agent Behavioral Cloning

Zhan, Eric and Zheng, Stephan and Yue, Yisong and Sha, Long and Lucey, Patrick (2018) Generative Multi-Agent Behavioral Cloning. . (Submitted)

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We propose and study the problem of generative multi-agent behavioral cloning, where the goal is to learn a generative, i.e., non-deterministic, multi-agent policy from pre-collected demonstration data. Building upon advances in deep generative models, we present a hierarchical policy framework that can tractably learn complex mappings from input states to distributions over multi-agent action spaces by introducing a hierarchy with macro-intent variables that encode long-term intent. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemDataset
Yue, Yisong0000-0001-9127-1989
Additional Information:This research is supported in part by NSF #1564330, NSF #1637598, and gifts from Bloomberg, Activision/Blizzard and Northrop Grumman. Dataset was provided by STATS:
Funding AgencyGrant Number
Bloomberg Data ScienceUNSPECIFIED
Northrop GrummanUNSPECIFIED
Record Number:CaltechAUTHORS:20190205-111434225
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92669
Deposited By: Tony Diaz
Deposited On:05 Feb 2019 19:35
Last Modified:02 Jun 2023 00:39

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