Stochastic prediction of multi-agent interactions from partial observations
Name
1902.09641.pdf
Description
Accepted version
Size
6.3 MB
Format
Adobe PDF
Checksum (MD5)
3be701152cc75972d09e7664d17f98a7
Author(s)
Sun, Chen
Karlsson, Per
Wu, Jiajun
Tenenbaum, Joshua B
Murphy, Kevin P
Date Issued
May 2019
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://openreview.net/forum?id=r1xdH3CcKX
