Generative Multi-Agent Behavioral Cloning

By Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey. 2018

41 citations recorded by the source corpus.

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Citations in the corpus

Stochastic Prediction of Multi-Agent Interactions from Partial Observations (2019, methodology): “We use the basketball data from Zhan et al. (2018).”
Relational Forward Models for Multi-Agent Learning (2018, positive): “…have been shown to successfully predict the forward dynamics of multi-agent systems, like basketball or soccer games, and to some extent, to provide insights into the relational and social structures present in the data (Hoshen, 2017; Kipf et al., 2018; Zhan et al., 2018; Zheng et al., 2017).”
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings (2019, background): “Multi-agent forecasting: Data-driven approaches have been applied to forecast complex interactions between multiple pedestrians [1, 3, 10, 14, 21], vehicles [6, 19, 26], and athletes [9, 18, 20, 34, 36, 37].”
On Multi-Agent Learning in Team Sports Games (2019, background): “More recently, (Zhan et al., 2018) provide a hierarchical generative model for achieving human gameplay using weak supervision.”
VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation (2020, background): “Beyond the autonomous driving domain, there is more general interest to predict the intents of interacting agents, such as for pedestrians [2, 13, 24], human activities [28] or for sports players [12, 26, 32, 33].”
TNT: Target-driveN Trajectory Prediction (2020, background): “Trajectory prediction has received much attention recently, especially in autonomous driving [9, 10, 12, 13, 14], in social interaction prediction [15, 16, 17, 18] and sports [19, 20].”
Learning From Demonstration in the Wild (2018, unknown): “The same idea has been used to imitate basketball teams [26], predict taxi driver behaviour [27], and control complex animations [28].”
Human motion trajectory prediction: a survey (2019, background): “…2018; Shi et al. 2019; Zhao et al. 2019; Hasan et al. 2018; Xue et al. 2018; Su et al. 2017; Sadeghian et al. 2018b), implicitly as a result of pixel-wise prediction (Walker et al. 2014), or by learning a joint policy (Ma et al. 2017; Lee et al. 2017; Shalev-Shwartz et al. 2016; Zhan et al. 2018).”