Results 21 to 30 of about 13,577 (270)

Mean-variance optimality for semi-Markov decision processes under first passage criteria [PDF]

, 2017
summary:This paper deals with a first passage mean-variance problem for semi-Markov decision processes in Borel spaces. The goal is to minimize the variance of a total discounted reward up to the system's first entry to some target set, where the ...
Xiangxiang Huang, Huang, Yonghui, Huang, Xiangxiang, Yonghui Huang   +3 more
core   +1 more source

Hierarchical dialogue optimization using semi-Markov decision processes [PDF]

Interspeech 2007, 2007
This paper addresses the problem of dialogue optimization on large search spaces. For such a purpose, in this paper we propose to learn dialogue strategies using multiple Semi-Markov Decision Processes and hierarchical reinforcement learning. This approach factorizes state variables and actions in order to learn a hierarchy of policies. Our experiments
Cuayáhuitl, Heriberto, Renals, Steve, Lemon, Oliver, Shimodaira, Hiroshi   +3 more
openaire   +2 more sources

Another set of verifiable conditions for average Markov decision processes with Borel spaces [PDF]

, 2015
summary:In this paper we give a new set of verifiable conditions for the existence of average optimal stationary policies in discrete-time Markov decision processes with Borel spaces and unbounded reward/cost functions. More precisely, we provide another
Guo, Xianping, Zou, Xiaolong
core   +4 more sources

First passage risk probability optimality for continuous time Markov decision processes [PDF]

, 2019
summary:In this paper, we study continuous time Markov decision processes (CTMDPs) with a denumerable state space, a Borel action space, unbounded transition rates and nonnegative reward function.
Wen, Xian, Huo, Haifeng
core   +1 more source

Learning to maximize reward rate: a model based on semi-Markov decision processes

Frontiers in Neuroscience, 2014
When animals have to make a number of decisions during a limited time interval, they face a fundamental problem: how much time they should spend on each decision in order to achieve the maximum possible total outcome.
Arash eKhodadadi, Pegah eFakhari, jerome eBusemeyer   +2 more
doaj   +1 more source

An Adaptive Scheduling Algorithm Integrating Hierarchical Reinforcement Learning and Semi-Markov Decision Processes

Applied Sciences
Coordinating multiple unmanned aerial vehicle (UAV) systems under strict energy and temporal constraints remains a complex scheduling problem. Existing reinforcement learning methods typically rely on fixed-time-step modeling, which struggles to ...
Feng Wang, Bingwei Ding, Fangchao Tian, Zhaohua Guo, Wenshuo Ma   +4 more
doaj   +1 more source

Method of Building a Model of Operational Changes for the Marine Combustion Engine Describing the Impact of the Damages of This Engine on the Characteristics of Its Operation Process

Polish Maritime Research, 2017
This article deals with the modeling of the processes of operating both marine main and auxiliary engines. The paper presents a model of changes in operating conditions of ship’s internal combustion engine.
Landowski Bogdan, Pająk Michał, Żółtowski Bogdan, Muślewski Łukasz   +3 more
doaj   +1 more source

Bridging the Synchrony Gap: A Deterministic GSMDP Approach for Integrating Deep Reinforcement Learning with Commercial Discrete Event Simulators

Proceedings of the Conference on Production Systems and Logistics
Although Deep Reinforcement Learning (DRL) offers adaptive control for manufacturing processes, training agents within high-fidelity commercial Discrete Event Simulation (DES) tools is hindered by the temporal mismatch between the continuous-time event ...
Varici, Merve Demir, Tercan, Hasan, Meisen, Tobias, Herberger, David, Hübner, Marco   +4 more
doaj   +1 more source

Post-earthquake performance recovery models for shield tunnels based on semi-Markov processes: development and application

Yantu gongcheng xuebao
To address the complexity and uncertainty inherent in the post-earthquake damage state recovery process of shield tunnels, a method for establishing performance recovery models based on semi-Markov processes is proposed, enabling quantitative assessment ...
ZENG Nianchen 1, HUANG Zhongkai 1, ZHANG Dongmei 1, 2, 3, GAN Binlin 1, 2
doaj   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source