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cs.LG2025
DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
Zongyue Li, Xiao Han, Yusong Li +2
Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforcement learning (RL). However, many existi…
cs.LG2024
Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
Collin Leiber, Niklas Strauß, Matthias Schubert +1
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years,…
cs.LG2024
Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
Niklas Strauß, Matthias Schubert
The traveling officer problem (TOP) is a challenging stochastic optimization task. In this problem, a parking officer is guided through a city equipped with parking sensors to fine…