Zobrazeno 1 - 10
of 891
pro vyhledávání: '"P. Moerland"'
Reinforcement learning (RL) is an appealing paradigm for training intelligent agents, enabling policy acquisition from the agent's own autonomously acquired experience. However, the training process of RL is far from automatic, requiring extensive hu
Externí odkaz:
http://arxiv.org/abs/2408.09807
Autor:
Ponse, Koen, Kleuker, Felix, Fejér, Márton, Serra-Gómez, Álvaro, Plaat, Aske, Moerland, Thomas
The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At every stage, new sequential decision-making challenges emerge, rangin
Externí odkaz:
http://arxiv.org/abs/2407.18597
The research field of automated negotiation has a long history of designing agents that can negotiate with other agents. Such negotiation strategies are traditionally based on manual design and heuristics. More recently, reinforcement learning approa
Externí odkaz:
http://arxiv.org/abs/2406.15096
The ability to perceive and reason about individual objects and their interactions is a goal to be achieved for building intelligent artificial systems. State-of-the-art approaches use a feedforward encoder to extract object embeddings and a latent g
Externí odkaz:
http://arxiv.org/abs/2402.03326
Autor:
Moerland, Thomas M., Müller-Brockhausen, Matthias, Yang, Zhao, Bernatavicius, Andrius, Ponse, Koen, Kouwenhoven, Tom, Sauter, Andreas, van der Meer, Michiel, Renting, Bram, Plaat, Aske
Due to the empirical success of reinforcement learning, an increasing number of students study the subject. However, from our practical teaching experience, we see students entering the field (bachelor, master and early PhD) often struggle. On the on
Externí odkaz:
http://arxiv.org/abs/2311.10590
Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by learning how to learn. In 2001, Hochreiter et al. showed that an LSTM
Externí odkaz:
http://arxiv.org/abs/2310.14139
Bell diagonal states constitute a well-studied family of bipartite quantum states that arise naturally in various contexts in quantum information. In this paper we generalize the notion of Bell diagonal states to the case of unequal local dimensions
Externí odkaz:
http://arxiv.org/abs/2308.10607
Autor:
Antoine H. C. van Kampen, Utkarsh Mahamune, Aldo Jongejan, Barbera D. C. van Schaik, Daria Balashova, Danial Lashgari, Mia Pras-Raves, Eric J. M. Wever, Adrie D. Dane, Rodrigo García-Valiente, Perry D. Moerland
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract Reproducibility of computational research is often challenging despite established guidelines and best practices. Translating these guidelines into practical applications remains difficult. Here, we present ENCORE, an approach to enhance tra
Externí odkaz:
https://doaj.org/article/c42772c7fef5476691f3d97c443e02ee
Autor:
van Ruissen MCE, van Kraaij SJ, Gal P, Bakker WA, Hijma HJ, Groeneveld GJ, de Kam ML, Burggraaf J, Moerland M
Publikováno v:
Journal of Experimental Pharmacology, Vol Volume 16, Pp 285-294 (2024)
Marella CE van Ruissen,1,2 Sebastiaan JW van Kraaij,1,2 Pim Gal,1,2 Wouter A Bakker,1,2 Hemme J Hijma,1,2 Geert Jan Groeneveld,1,2 Marieke L de Kam,1 Jacobus Burggraaf,1,2 Matthijs Moerland1,2 1Center for Human Drug Research, Leiden, The Netherlands;
Externí odkaz:
https://doaj.org/article/7675049fff3140fca06da4a0dda34558
Model-based reinforcement learning (MBRL) has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models, it is possible to learn compact and generalizable models from data. I
Externí odkaz:
http://arxiv.org/abs/2306.00840