32 citations · 200 across the 28 of their papers we have counts for
8 papers · 1 filter
Quantum Policy Gradient Algorithm with Optimized Action Decoding
Nico Meyer, Daniel D. Scherer, Axel Plinge +2
Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on ap…
A Survey on Quantum Reinforcement Learning
Nico Meyer, Christian Ufrecht, Maniraman Periyasamy +3
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on…
Efficient Beam Search for Initial Access Using Collaborative Filtering
George Yammine, Georgios Kontes, Norbert Franke +2
Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest powe…
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving
Sebastian Rietsch, Shih-Yuan Huang, Georgios Kontes +2
Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks. However, unlike supervised machine learning, learning strategies that gen…
Incremental Data-Uploading for Full-Quantum Classification
Maniraman Periyasamy, Nico Meyer, Christian Ufrecht +3
The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy i…
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
Lukas M. Schmidt, Sebastian Rietsch, Axel Plinge +2
Autonomous driving has the potential to revolutionize mobility and is hence an active area of research. In practice, the behavior of autonomous vehicles must be acceptable, i.e., e…
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility
Lukas M. Schmidt, Johanna Brosig, Axel Plinge +2
Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement L…
Uncovering Instabilities in Variational-Quantum Deep Q-Networks
Maja Franz, Lucas Wolf, Maniraman Periyasamy +5
Deep Reinforcement Learning (RL) has considerably advanced over the past decade. At the same time, state-of-the-art RL algorithms require a large computational budget in terms of t…