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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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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.Abstract:
From the Publisher:
The past decade has seen considerable theoretical and applied research on Markov decision processes, as well as the growing use of these models in ecology, economics, communications engineering, and other fields where outcomes are uncertain and sequential decision-making processes are needed. A timely response to this increased activity, Martin L. Puterman's new work provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models. It discusses all major research directions in the field, highlights many significant applications of Markov decision processes models, and explores numerous important topics that have previously been neglected or given cursory coverage in the literature. Markov Decision Processes focuses 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. The book is organized around optimality criteria, using a common framework centered on the optimality (Bellman) equation for presenting results. The results are presented in a "theorem-proof" format and elaborated on through both discussion and examples, including results that are not available in any other book. A two-state Markov decision process model, presented in Chapter 3, is analyzed repeatedly throughout the book and demonstrates many results and algorithms. Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria. It also explores several topics that have received little or no attention in other books, including modified policy iteration, multichain models with average reward criterion, and sensitive optimality. In addition, a Bibliographic Remarks section in each chapter comments on relevant historicread more
Citations
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Resource Allocation in Vehicular Cloud Computing Systems With Heterogeneous Vehicles and Roadside Units
TL;DR: Simulation shows that the resource allocation in the VCC system can be captured by the proposed model, which performs well in terms of long-term expected values (consisting of consumption costs of power and time), under various parameter settings.
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Randomized shortest-path problems: Two related models
TL;DR: This work revisits Akamatsu's model by recasting it into a sum-over-paths statistical physics formalism allowing easy derivation of all the quantities of interest in an elegant, unified way and shows that the unique optimal policy can be obtained by solving a simple linear system of equations.
Posted Content
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
TL;DR: In this article, a factored Markov decision process (MDP) with very large state spaces is considered, and an algorithm is proposed to find policies that are approximately optimal with respect to the original MDP.
Journal ArticleDOI
Rapid specification and automated generation of prompting systems to assist people with dementia
TL;DR: This paper describes a knowledge-driven method for automatically generating POMDP activity recognition and context-sensitive prompting systems for complex tasks and makes the case that the method could feasibly be used in a clinical or industrial setting.
Journal ArticleDOI
Optimal Policies for a Capacitated Two-Echelon Inventory System
TL;DR: This paper demonstrates optimal policies for capacitated serial multiechelon production/inventory systems by demonstrating that a modified echelon base-stock policy is optimal in a two-stage system when there is a smaller capacity at the downstream facility.
References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI
Finding Optimal (s, S) Policies Is About As Simple As Evaluating a Single Policy
Yu-Sheng Zheng,Awi Federgruen +1 more
TL;DR: A new algorithm for computing optimal ( s , S ) policies is derived based upon a number of new properties of the infinite horizon cost function c as well as a new upper bound for optimal order-up-to levels S * and a new lower bound for ideal reorder levels s *.