3 papers
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.…
cs.LG2024
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt +1
In offline reinforcement learning, a policy is learned using a static dataset in the absence of costly feedback from the environment. In contrast to the online setting, only using…