81 citations · 197 across the 18 of their papers we have counts for
18 papers
Surrogate Fitness Metrics for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
We employ an evolutionary optimization framework that perturbs initial states to generate informative and diverse policy demonstrations. A joint surrogate fitness function guides t…
Optimizing Sensor Redundancy in Sequential Decision-Making Problems
Jonas Nüßlein, Maximilian Zorn, Fabian Ritz +5
Reinforcement Learning (RL) policies are designed to predict actions based on current observations to maximize cumulative future rewards. In real-world applications (i.e., non-simu…
Emergence in Multi-Agent Systems: A Safety Perspective
Philipp Altmann, Julian Schönberger, Steffen Illium +5
Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior…
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
Philipp Altmann, Céline Davignon, Maximilian Zorn +3
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of…
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Michael Kölle, Yannick Erpelding, Fabian Ritz +3
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments. In particular, the predator-pre…
Quantum Advantage Actor-Critic for Reinforcement Learning
Michael Kölle, Mohamad Hagog, Fabian Ritz +4
Quantum computing offers efficient encapsulation of high-dimensional states. In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Ac…
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Philipp Altmann, Fabian Ritz, Leonard Feuchtinger +3
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a…
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
Philipp Altmann, Thomy Phan, Fabian Ritz +2
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we intr…
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Thomy Phan, Fabian Ritz, Philipp Altmann +5
Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research…
Capturing Dependencies within Machine Learning via a Formal Process Model
Fabian Ritz, Thomy Phan, Andreas Sedlmeier +7
The development of Machine Learning (ML) models is more than just a special case of software development (SD): ML models acquire properties and fulfill requirements even without di…
Acoustic Leak Detection in Water Networks
Robert Müller, Steffen Illium, Fabian Ritz +4
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 depl…
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
Fabian Ritz, Thomy Phan, Robert Müller +8
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they m…
Analysis of Feature Representations for Anomalous Sound Detection
Robert Müller, Steffen Illium, Fabian Ritz +1
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection. In doing so, we leverage the knowledge that is…
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
Robert Müller, Fabian Ritz, Steffen Illium +1
In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Con…
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
Thomas Gabor, Leo Sünkel, Fabian Ritz +5
We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them…
Soccer Team Vectors
Robert Müller, Stefan Langer, Fabian Ritz +3
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 resu…
Deep Neural Baselines for Computational Paralinguistics
Daniel Elsner, Stefan Langer, Fabian Ritz +2
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-e…
The Impact of Uncle Rewards on Selfish Mining in Ethereum
Fabian Ritz, Alf Zugenmaier
Many of today's crypto currencies use blockchains as decentralized ledgers and secure them with proof of work. In case of a fork of the chain, Bitcoin's rule for achieving consensu…