Time-Aware World Model for Adaptive Prediction and Control
Anh N Nhu, Sanghyun Son, Ming Lin
Abstract
In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, $\Delta t$, and training over a diverse range of $\Delta t$ values – rather than sampling at a fixed time-step – TAWM learns both high- and low-frequency task dynamics across diverse control problems. Grounded in the information-theoretic insight that the optimal sampling rate depends on a system’s underlying dynamics, this time-aware formulation improves both performance and data efficiency. Empirical evaluations show that TAWM consistently outperforms conventional models across varying observation rates in a variety of control tasks, using the same number of training samples and iterations. Our code can be found online at: github.com/anh-nn01/Time-Aware-World-Model.
BibTeX
@inproceedings{
nhu2025timeaware,
title={Time-Aware World Model for Adaptive Prediction and Control},
author={Anh N Nhu and Sanghyun Son and Ming Lin},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=gZ5N3TLjwv}
}