  This work investigates the problem of multi-agents trajectory prediction.
Prior approaches lack of capability of capturing fine-grained dependencies
among coordinated agents. In this paper, we propose a spatial-temporal
trajectory prediction approach that is able to learn the strategy of a team
with multiple coordinated agents. In particular, we use graph-based attention
model to learn the dependency of the agents. In addition, instead of utilizing
the recurrent networks (e.g., VRNN, LSTM), our method uses a Temporal
Convolutional Network (TCN) as the sequential model to support long effective
history and provide important features such as parallelism and stable
gradients. We demonstrate the validation and effectiveness of our approach on
two different sports game datasets: basketball and soccer datasets. The result
shows that compared to related approaches, our model that infers the dependency
of players yields substantially improved performance. Code is available at
https://github.com/iHeartGraph/predict
