Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Keywords: sequence modeling, efficient training, efficient inference, spatio-temporal, multi-agent task
TL;DR: We propose an efficient sequence modeling algorithm for irregular observation data.
Abstract: Various works have aimed at combining the inference efficiency of recurrent models and training parallelism of MHA for sequence modeling. However, most of these works focus on tasks with fixed-dimension observation spaces, such as individual tokens in language modeling or pixels in image completion. Variably sized, irregular observation spaces are relatively under-represented, yet they occur frequently in multi-agent domains such as autonomous driving and human-robot interaction. To handle an observation space of varying size, we propose a novel algorithm that alternates between cross-attention between a 2D latent state and observation, and a discounted cumulative sum over the sequence dimension to efficiently accumulate historical information. We find this resampling cycle is critical for performance. To evaluate efficient sequence modeling in this domain, we introduce two multi-agent intention tasks: simulated agents chasing bouncing particles and micromanagement analysis in professional StarCraft II games. Our algorithm achieves comparable accuracy with a lower parameter count, faster training and inference compared to existing methods.
Supplementary Material: zip
Primary Area: learning on time series and dynamical systems
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Submission Number: 14157
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