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most citedConstrained Latent Action Policies for Model-Based Offline Reinforcement Learning

1 citations · 1 across the 4 of their papers we have counts for

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cs.LG2025

Latent Action World Models for Control with Unlabeled Trajectories

Marvin Alles, Xingyuan Zhang, Patrick van der Smagt +1

Inspired by how humans combine direct interaction with action-free experience (e.g., videos), we study world models that learn from heterogeneous data. Standard world models typica…

cs.LG2025

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning

Marvin Alles, Nutan Chen, Patrick van der Smagt +1

The use of guidance to steer sampling toward desired outcomes has been widely explored within diffusion models, especially in applications such as image and trajectory generation.…