7 citations · 18 across the 26 of their papers we have counts for
3 papers · 2 filters
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…
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…
Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
Hector Kohler, Quentin Delfosse, Waris Radji +2
There exist applications of reinforcement learning like medicine where policies need to be ''interpretable'' by humans. User studies have shown that some policy classes might be mo…