Zhan, Eric and Zheng, Stephan and Yue, Yisong and Sha, Long and Lucey, Patrick (2018) Generative Multi-Agent Behavioral Cloning. . (Submitted) https://resolver.caltech.edu/CaltechAUTHORS:20190205-111434225
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Abstract
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) | ||||||||||||
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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: https://www.stats.com/data-science/. | ||||||||||||
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DOI: | 10.48550/arXiv.1803.07612 | ||||||||||||
Record Number: | CaltechAUTHORS:20190205-111434225 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190205-111434225 | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 92669 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | Tony Diaz | ||||||||||||
Deposited On: | 05 Feb 2019 19:35 | ||||||||||||
Last Modified: | 02 Jun 2023 00:39 |
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