4 papers
Constrained Reinforcement Learning Using Successor Representations
Michael Girstl, Alexander Mattick, Christopher Mutschler
Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy. A common way to model such constraints is to introduce an additional cost si…
The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding
Christian Wielenberg, Lucas Heublein, Jonathan Ott +8
Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust. Robust i…
Meta-Learning Multi-armed Bandits for Beam Tracking in 5G and 6G Networks
Alexander Mattick, George Yammine, Georgios Kontes +2
Beamforming-capable antenna arrays with many elements enable higher data rates in next generation 5G and 6G networks. In current practice, analog beamforming uses a codebook of pre…
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
Alexander Mattick, Maniraman Periyasamy, Christian Ufrecht +4
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-…