79 citations · 370 across the 33 of their papers we have counts for
42 papers
Variational Quantum Circuits in Offline Contextual Bandit Problems
Lukas Schulte, Daniel Hein, Steffen Udluft +1
This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Ben…
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning
Batıkan Bora Ormancı, Phillip Swazinna, Steffen Udluft +1
In this paper, we investigate offline reinforcement learning (RL) with the goal of training a single robust policy that generalizes effectively across environments with unseen dyna…
On-device Online Learning and Semantic Management of TinyML Systems
Haoyu Ren, Xue Li, Darko Anicic +1
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of…
Model-based Offline Quantum Reinforcement Learning
Simon Eisenmann, Daniel Hein, Steffen Udluft +1
This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the poli…
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
Haoyu Ren, Xue Li, Darko Anicic +1
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence o…
Automatic Trade-off Adaptation in Offline RL
Phillip Swazinna, Steffen Udluft, Thomas Runkler
Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade…