1 citations · 2 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
Dennis Gross, Helge Spieker, Arnaud Gotlieb +1
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes,…
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies
Dennis Gross, Helge Spieker
We introduce a method to verify stochastic reinforcement learning (RL) policies. This approach is compatible with any RL algorithm as long as the algorithm and its corresponding en…