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Simplifying Model-based RL: Learning Representations, Latent-space Models and Policies with One Objective
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2026-02-22 17:19:27
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Simplifying Model-based RL: Learning Representations, Latent-space Models and Policies with One Objective Simplifying Model-based RL: Learning Representations, Latent-space Models and Policies with One Objective Raj Ghughare 1,2 , Homanga Bharadhwaj 2 , Benjamin Eysenbach 2 , Sergey Levine 3 , Ruslan Salakhutdinov 2 , 1 VNIT, 2 Carnegie Mellon University, 3 UC Berkeley Paper Code Abstract While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from a high dimensional sensors can be challenging. To address this, prior work has instead learned low-dimensional representation of observations, through auxiliary objectives like reconstruction or value prediction, where their precise alignment with the RL objective often remains unclear. In this work, we propose a single objective for jointly optimizing these rep...
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