17 citations · 67 across the 16 of their papers we have counts for
12 papers · 1 filter
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…
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…
TinyReptile: TinyML with Federated Meta-Learning
Haoyu Ren, Darko Anicic, Thomas A. Runkler
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of t…
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Daniel Hein +1
Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We…