March 14, 2024, 4:43 a.m. | Yi Tian, Kaiqing Zhang, Russ Tedrake, Suvrit Sra

cs.LG updates on

arXiv:2212.14511v2 Announce Type: replace
Abstract: We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a direct latent model learning approach, where a dynamic model in some latent state space is learned by predicting quantities directly related to planning (e.g., costs) without reconstructing the observations. In particular, we focus on an intuitive cost-driven state representation learning method for solving Linear Quadratic Gaussian (LQG) control, one of …

abstract arxiv control cs.lg dynamic linear math.oc observable solve space state study type

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