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Stochastic prediction of multi-agent interactions from partial observations

Author(s)
Sun, Chen; Karlsson, Per; Wu, Jiajun; Tenenbaum, Joshua B; Murphy, Kevin P
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.
Date issued
2019-05
URI
https://hdl.handle.net/1721.1/126593
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
ICLR 2019: International Conference on Learning Representations
Publisher
International Conference on Learning Representations
Citation
Sun, Chen et al. "Stochastic prediction of multi-agent interactions from partial observations." ICLR 2019: 7th International Conference on Learning Representations, May 6-9, 2019, New Orleans, Louisiana: https://openreview.net/forum?id=r1xdH3CcKX ©2019
Version: Author's final manuscript

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