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... |t Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks / |r Siegmund Duell, Steffen Udluft and Volkmar Sterzing -- |t ...
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... Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks /; Siegmund Duell, Steffen Udluft and Volkmar Sterzing ...
Neural Networks: Tricks of the Trade - Google Books
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... Reinforcement Learning Problems with Recurrent Neural Networks Siegmund Duell, Steffen Udluft, and Volkmar Sterzing Steps ...
On Querying for Safe Optimality in Factored Markov Decision ifaamasifaamas.org/Proceedings/aamas2018/pdfs/p2168.pdf
ifaamas.org
[8] Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, and Steffen Udluft Safe exploration for reinforcement learning.
PUBLIKATIONEN: Institut für Neuro- und Bioinformatik
www.inb.uni-luebeck.de
Daniel Schneegass and Steffen Udluft and Thomas Martinetz: Uncertainty Propagation for Quality Assurance in Reinforcement Learning. in Proc. of the International Joint Conference on Neural Networks, pp , 2008
Uncertainty Propagation for Quality Assurance in ...
webmail.inb.uni-luebeck.de
Daniel Schneegass, Steffen Udluft, and Thomas Martinetz Senior Member, IEEE Abstract—In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking into account the derived Q-function’s uncertainty, which stems
Mitarbeiter / Externe Doktoranden
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Anton Maximilian Schaefer, Daniel Schneegaß, Volkmar Sterzing, and Steffen Udluft. A Neural Reinforcement Learning Approach to Gas Turbine Control.
Staff / External Doctorands
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Anton Maximilian Schaefer, Daniel Schneegaß, Volkmar Sterzing, and Steffen Udluft. A Neural Reinforcement Learning Approach to Gas Turbine Control.
3
ebooks.iospress.nl
Alexander Hans, Steffen Udluft. Pages DOI Abstract. Reinforcement learning aims to derive an optimal policy for ...
Alle Infos zum Namen "Steffen Udluft"
Solving Partially Observable Reinforcement Learning Problems with...
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The aim of this chapter is to provide a series of tricks and recipes for neural state estimation, particularly for real world applications of
Reinforcement Learning with Particle Swarm Optimization Policy...
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Steffen Udluft, Siemens AG, Munich, Germany. ABSTRACT. This article introduces a model-based reinforcement learning (RL) approach for continuous state.
A Recurrent Control Neural Network for Data Efficient Reinforcement...
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... Control Neural Network for Data Efficient Reinforcement Learning. more. COLLAPSE. Anton Maximilian Schaefer, Steffen Udluft, Hans-Georg Zimmermann.
Alexander Hans
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Alexander Hans, Steffen Udluft · Artificial Neural Networks – ICANN > Learning Algorithms. In a typical reinforcement learning (RL) setting details of the ...
Bayesian Neural Networks with Random Inputs for Model Based...
towardsdatascience.com
I describe here our recent ICLR paper [1] [code] [talk], which introduces a novel method for model-based reinforcement learning. The main author of this work...
A Benchmark Environment Motivated by Industrial Control Problems
scirate.com
... Stefan Depeweg,; Michel Tokic,; Steffen Udluft,; Alexander Hentschel,; Thomas A. Runkler,; Volkmar Sterzing. In the research area of reinforcement learning (RL) , frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen ...
IOS Press Ebooks - Uncertainty Propagation for Efficient Exploration...
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Reinforcement Learning. Authors. Alexander Hans, Steffen Udluft. Pages
Uncertainty in Reinforcement Learning - Awareness, Quantisation, and...
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Uncertainty in Reinforcement Learning - Awareness, Quantisation, and Control. By Daniel Schneegass, Alexander Hans and Steffen Udluft. Published: August ...
[ ] Learning and Policy Search in Stochastic Dynamical...
arxiv-export-lb.library.cornell.edu
Authors: Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft. (Submitted on 23 May (v1), last revised 8 Mar (this version, v3)). Abstract: We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and ...
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