no code implementations • 4 Apr 2024 • Philipp Altmann, Céline Davignon, Maximilian Zorn, Fabian Ritz, Claudia Linnhoff-Popien, Thomas Gabor
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT).
no code implementations • 13 Jan 2024 • Michael Kölle, Mohamad Hgog, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Stein, Claudia Linnhoff-Popien
In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components.
1 code implementation • 13 Jan 2024 • Michael Kölle, Yannick Erpelding, Fabian Ritz, Thomy Phan, Steffen Illium, Claudia Linnhoff-Popien
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
1 code implementation • 26 Apr 2023 • Philipp Altmann, Fabian Ritz, Leonard Feuchtinger, Jonas Nüßlein, Claudia Linnhoff-Popien, Thomy Phan
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
no code implementations • 18 Jan 2023 • Philipp Altmann, Thomy Phan, Fabian Ritz, Thomas Gabor, Claudia Linnhoff-Popien
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
no code implementations • 10 Aug 2022 • Fabian Ritz, Thomy Phan, Andreas Sedlmeier, Philipp Altmann, Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein, Claudia Linnhoff-Popien, Thomas Gabor
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
1 code implementation • NeurIPS 2021 • Thomy Phan, Fabian Ritz, Lenz Belzner, Philipp Altmann, Thomas Gabor, Claudia Linnhoff-Popien
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
1 code implementation • ALIFE 2021 • Fabian Ritz, Daniel Ratke, Thomy Phan, Lenz Belzner, Claudia Linnhoff-Popien
This paper considers sustainable and cooperative behavior in multi-agent systems.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Dec 2020 • Fabian Ritz, Thomy Phan, Robert Müller, Thomas Gabor, Andreas Sedlmeier, Marc Zeller, Jan Wieghardt, Reiner Schmid, Horst Sauer, Cornel Klein, Claudia Linnhoff-Popien
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 11 Dec 2020 • Robert Müller, Steffen Illium, Fabian Ritz, Kyrill Schmid
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection.
no code implementations • 11 Dec 2020 • Robert Müller, Steffen Illium, Fabian Ritz, Tobias Schröder, Christian Platschek, Jörg Ochs, Claudia Linnhoff-Popien
In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment.
no code implementations • 5 Jun 2020 • Robert Müller, Fabian Ritz, Steffen Illium, Claudia Linnhoff-Popien
In industrial applications, the early detection of malfunctioning factory machinery is crucial.
no code implementations • 29 Apr 2020 • Thomas Gabor, Leo Sünkel, Fabian Ritz, Thomy Phan, Lenz Belzner, Christoph Roch, Sebastian Feld, Claudia Linnhoff-Popien
We discuss the synergetic connection between quantum computing and artificial intelligence.
no code implementations • 30 Jul 2019 • Robert Müller, Stefan Langer, Fabian Ritz, Christoph Roch, Steffen Illium, Claudia Linnhoff-Popien
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
no code implementations • 5 Jul 2019 • Daniel Elsner, Stefan Langer, Fabian Ritz, Robert Müller, Steffen Illium
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).