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pro vyhledávání: '"Klarmann, Noah"'
This paper explores the potential application of Deep Reinforcement Learning in the furniture industry. To offer a broad product portfolio, most furniture manufacturers are organized as a job shop, which ultimately results in the Job Shop Scheduling
Externí odkaz:
http://arxiv.org/abs/2409.11820
Autor:
Mehta, Devang, Klarmann, Noah
Manufacturing industries require efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precisio
Externí odkaz:
http://arxiv.org/abs/2309.06884
Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance with these state augmentations, delay-resolved reinforcement learning algorithm
Externí odkaz:
http://arxiv.org/abs/2306.09010
The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy but also for increasing the overall efficiency. Among the different job scheduling problems, th
Externí odkaz:
http://arxiv.org/abs/2302.13941
Autor:
Josifovski, Josip, Malmir, Mohammadhossein, Klarmann, Noah, Žagar, Bare Luka, Navarro-Guerrero, Nicolás, Knoll, Alois
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system
Externí odkaz:
http://arxiv.org/abs/2206.06282
Autor:
Klarmann, Noah
This work provides a starting point for researchers interested in gaining a deeper understanding of the big picture of artificial intelligence (AI). To this end, a narrative is conveyed that allows the reader to develop an objective view on current d
Externí odkaz:
http://arxiv.org/abs/2103.11961
Autor:
Mehta, Devang, Klarmann, Noah
Publikováno v:
Machine Learning & Knowledge Extraction; Mar2024, Vol. 6 Issue 1, p1-17, 17p
Autor:
Klarmann, Noah, Malmir, Mohammadhossein, Josifovski, Josip, Plorin, Daniel, Wagner, Matthias, Knoll, Alois
Publikováno v:
Artificial Intelligence for Digitising Industry – Applications ISBN: 9781003337232
This paper outlines the concept of optimising trajectories for industrial robots by applying deep reinforcement learning in simulations. An application of high technical relevance is considered in a production line of an automotive manufacturer (AUDI
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::01b328698954e10d58681894bf51c1c2
https://doi.org/10.1201/9781003337232-5
https://doi.org/10.1201/9781003337232-5
Autor:
Josifovski, Josip, Malmir, Mohammadhossein, Klarmann, Noah, Nica, Iulia, Wotawa, Franz, Klueck, Florian, Felbinger, Hermann, Trantidou, Tatiana, Marini, Eleftheria, Schneider, Mathias, Jokela, Tuomas, Chromý, Adam, Daskalopoulos, Ioannis, Trouva, Eleni, Poulakidas, Athanasios, Lucas, Peter
The present document is a deliverable of the AI4DI project, which is co-funded by the ECSEL Joint Undertaking under grant agreement No. 826060 and ECSEL JU Member States. This report gives an overview of the simulation and modelling approaches at the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1a1e3245f845641eeb13ecd5327f7fe7