Journal Article10.2307/2348465
Markov Decision Processes.
Stephen Brooks,Douglas J. White +1 more
132
About: This article is published in The Statistician. The article was published on 01 Jan 1995. The article focuses on the topics: Markov decision process.
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Citations
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
Martin L. Puterman
- 15 Apr 1994
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
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Risk-averse dynamic programming for Markov decision processes
TL;DR: The concept of a Markov risk measure is introduced and it is used to formulate risk-averse control problems for two Markov decision models: a finite horizon model and a discounted infinite horizon model.
The Cross-Entropy Method for Continuous Multi-Extremal Optimization
TL;DR: The effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints, is demonstrated.
Transmission Policies for Energy Harvesting Sensors with Time-Correlated Energy Supply
TL;DR: This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver, and derives the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced.
160
MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization
Runyu Shi,Jia Zhang,Wenjing Chu,Qihao Bao,Xiatao Jin,Chenran Gong,Qihao Zhu,Chang Yu,Steven Rosenberg +8 more
- 27 Jun 2015
TL;DR: Markov Decision Process (MDP) is applied to the NP-hard problem to dynamically allocate cloud resources for NFV components and Bayesian learning method is applications to monitor the historical resource usage in order to predict future resource reliability.
91
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