1 citations · 1 across the 4 of their papers we have counts for
4 papers
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking--a tec…
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Pruning neural networks (NNs) can streamline them but risks removing vital parameters from safe reinforcement learning (RL) policies. We introduce an interpretable RL method called…
Efficient Milling Quality Prediction with Explainable Machine Learning
Dennis Gross, Helge Spieker, Arnaud Gotlieb +2
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study empl…
Enhancing RL Safety with Counterfactual LLM Reasoning
Dennis Gross, Helge Spieker
Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-trai…