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cs.LG2026
Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning
Fernando Palafox, David Fridovich-Keil
World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate. We study the problem of adapting a world mode…
cs.LG2026
Generalized Information Gathering Under Dynamics Uncertainty
Fernando Palafox, Jingqi Li, Jesse Milzman +1
An agent operating in an unknown dynamical system must learn its dynamics from observations. Active information gathering accelerates this learning, but existing methods derive bes…
cs.LG2025
Data-Driven Modeling and Correction of Vehicle Dynamics
Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj +4
We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exh…