Reinforcement learning for thermal and reliability management in manycore systems

II Weber, VB Zanini, FG Moraes - Design Automation for Embedded …, 2025 - Springer
The advent of manycore systems has led to the need for efficient dynamic thermal and
reliability management techniques to increase system reliability. Increasing power density …

Modeling and simulation of Fuzzy-rule based WBAN using multi-level fuzzy colored petri-nets and reinforcement learning

N Majma, SM Babamir - Journal of Computational Science, 2024 - Elsevier
Abstract By developing IoTs (Internet of Things), a new concept is arising in WBANs
(Wireless Body Area Networks), called IoBs (Internet of Bodies). WBANs are the safety …

Topological Foundations of Reinforcement Learning

DK Kadurha - arXiv preprint arXiv:2410.03706, 2024 - arxiv.org
The goal of this work is to serve as a foundation for deep studies of the topology of state,
action, and policy spaces in reinforcement learning. By studying these spaces from a …

Produktionssynergien der Zukunft: Innovationskraft durch Additive Fertigung und Künstliche Intelligenz

M Göldner, L Siebert, J Hüllemann… - Zeitschrift für …, 2023 - degruyter.com
Die hybride Prozesskette aus der draht-und lichtbogenbasierten Additiven Fertigung und
der spanenden Nachbearbeitung bringt neue Herausforderungen in der Prozessauslegung …

Decreasing the number of demonstrations required for Inverse Reinforcement Learning by integrating human feedback

Z Oğurlu - 2024 - repository.tudelft.nl
The main concept behind reinforcement learning is that an agent takes certain actions and is
rewarded or punished for these actions. However, the rewards that are involved when …