14 citations · 23 across the 4 of their papers we have counts for
4 papers
Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task
Reuf Kozlica, Stefan Wegenkittl, Simon Hirländer
This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated pro…
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications
Reuf Kozlica, Georg Schäfer, Simon Hirländer +1
This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and…
An Architecture for Deploying Reinforcement Learning in Industrial Environments
Georg Schäfer, Reuf Kozlica, Stefan Wegenkittl +1
Industry 4.0 is driven by demands like shorter time-to-market, mass customization of products, and batch size one production. Reinforcement Learning (RL), a machine learning paradi…
Sequential IoT Data Augmentation using Generative Adversarial Networks
Maximilian Ernst Tschuchnig, Cornelia Ferner, Stefan Wegenkittl
Sequential data in industrial applications can be used to train and evaluate machine learning models (e.g. classifiers). Since gathering representative amounts of data is difficult…