Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Wiering, Marco"'
Autor:
Kaufmann, Thomas, Castela Forte, José, Hiemstra, Bart, Wiering, Marco A, Grzegorczyk, Marco, Epema, Anne H, van der Horst, Iwan C C
Publikováno v:
JMIR Medical Informatics, Vol 7, Iss 4, p e15358 (2019)
BackgroundHemodynamic assessment of critically ill patients is a challenging endeavor, and advanced monitoring techniques are often required to guide treatment choices. Given the technical complexity and occasional unavailability of these techniques,
Externí odkaz:
https://doaj.org/article/98ddf83ec4014c1089dc631a65c2f52e
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes to address this issue by re-using knowledge from previously learned t
Externí odkaz:
http://arxiv.org/abs/2205.14410
Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort b
Externí odkaz:
http://arxiv.org/abs/2109.04847
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows agents to be much more data-efficient, as it enables them to learn behavi
Externí odkaz:
http://arxiv.org/abs/2108.06526
Autor:
Müller, Arthur, Rangras, Vishal, Schnittker, Georg, Waldmann, Michael, Friesen, Maxim, Ferfers, Tobias, Schreckenberg, Lukas, Hufen, Florian, Jasperneite, Jürgen, Wiering, Marco
Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to
Externí odkaz:
http://arxiv.org/abs/2103.16223
The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, three state-of-the-art algorithms, namely Convolutional Neural Networks, D
Externí odkaz:
http://arxiv.org/abs/2011.00485
In many reinforcement learning (RL) problems, it takes some time until a taken action by the agent reaches its maximum effect on the environment and consequently the agent receives the reward corresponding to that action by a delay called action-effe
Externí odkaz:
http://arxiv.org/abs/2010.15597
Autor:
Holubar, Mario S., Wiering, Marco A.
In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control the acceleration and steering of a car while navigating a randomly gene
Externí odkaz:
http://arxiv.org/abs/2001.05270
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly learn an approximation of the state-value function ($V$), alongside a
Externí odkaz:
http://arxiv.org/abs/1909.01779
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and uses this network for training a second Quality-value network that le
Externí odkaz:
http://arxiv.org/abs/1810.00368