81 citations · 197 across the 18 of their papers we have counts for
5 papers · 1 filter
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…