2 citations · 2 across the 4 of their papers we have counts for
4 papers
Deep Reinforcement Learning Agents are not even close to Human Intelligence
Quentin Delfosse, Jannis Blüml, Fabian Tatai +6
Deep reinforcement learning (RL) agents achieve impressive results in a wide variety of tasks, but they lack zero-shot adaptation capabilities. While most robustness evaluations fo…
Better Decisions through the Right Causal World Model
Elisabeth Dillies, Quentin Delfosse, Jannis Blüml +3
Reinforcement learning (RL) agents have shown remarkable performances in various environments, where they can discover effective policies directly from sensory inputs. However, the…
Deep Reinforcement Learning via Object-Centric Attention
Jannis Blüml, Cedric Derstroff, Bjarne Gregori +3
Deep reinforcement learning agents, trained on raw pixel inputs, often fail to generalize beyond their training environments, relying on spurious correlations and irrelevant backgr…
Investigating Exit Choice in Built Environment Evacuation combining Immersive Virtual Reality and Discrete Choice Modelling
R Lovreglio, E Dillies, E Kuligowski +2
In the event of a fire emergency in the built environment, occupants face a range of evacuation decisions, including the choice of exits. An important question from the standpoint…