Video Occupancy Models

• URL: http://arxiv.org/abs/2407.09533v1
• Date: Tue, 25 Jun 2024 17:57:38 GMT
• Title: Video Occupancy Models
• Authors: Manan Tomar, Philippe Hansen-Estruch, Philip Bachman, Alex Lamb, John Langford, Matthew E. Taylor, Sergey Levine,
• Abstract summary: Video Occupancy models (VOCs) operate in a compact latent space. Unlike prior latent-space world models, VOCs directly predict the discounted distribution of future states in a single step.
• Score: 59.17330408925321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact latent space, thus avoiding the need to make predictions about individual pixels. Unlike prior latent-space world models, VOCs directly predict the discounted distribution of future states in a single step, thus avoiding the need for multistep roll-outs. We show that both properties are beneficial when building predictive models of video for use in downstream control. Code is available at \href{https://github.com/manantomar/video-occupancy-models}{\texttt{github.com/manantomar/video-occupancy-models}}.

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