Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task

no code implementations • 2 Jun 2023 • 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 production system.

Q-Learning Reinforcement Learning (RL)

A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications

no code implementations • 2 Jun 2023 • Reuf Kozlica, Georg Schäfer, Simon Hirländer, Stefan Wegenkittl

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 batch size one production.

reinforcement-learning Reinforcement Learning (RL)

An Architecture for Deploying Reinforcement Learning in Industrial Environments

no code implementations • 2 Jun 2023 • Georg Schäfer, Reuf Kozlica, Stefan Wegenkittl, Stefan Huber

Industry 4. 0 is driven by demands like shorter time-to-market, mass customization of products, and batch size one production.

reinforcement-learning Reinforcement Learning (RL)

Isotropic Contextual Representations through Variational Regularization

no code implementations • 29 Sep 2021 • Cornelia Ferner, Stefan Wegenkittl

However, these representations have been shown to suffer from the degeneration problem, i. e. they occupy a narrow cone in the latent space.

Decoder Sentence

Sequential IoT Data Augmentation using Generative Adversarial Networks

no code implementations • 13 Jan 2021 • Maximilian Ernst Tschuchnig, Cornelia Ferner, Stefan Wegenkittl

The positive results from the evaluation support the initial assumption that generating sequential data from a small ground truth is possible.

BIG-bench Machine Learning Data Augmentation