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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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Journal ArticleDOI
Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
TL;DR: This work forms a Markov decision process model of the stochastic inventory routing problem and proposes approximation methods to find good solutions with reasonable computational effort and indicates how the proposed approach can be used for other Markov decisions involving the control of multiple resources.
Posted Content
A unified view of entropy-regularized Markov decision processes
TL;DR: A general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes (MDPs) is proposed, showing that using the conditional entropy of the joint state-action distributions as regularization yields a dual optimization problem closely resembling the Bellman optimality equations.
Journal ArticleDOI
The computational complexity of probabilistic planning
TL;DR: A new basic NPPP -complete problem is introduced, E-MAJSAT, which generalizes the standard Boolean satisfiability problem to computations involving probabilistic quantities and suggests that the development of good heuristics for E- MAJSAT could be important for the creation of efficient algorithms for a wide variety of problems.
Learning Maps for Indoor Mobile Robot Navigation.
Sebastian Thrun,Arno Buecken +1 more
TL;DR: By combining both paradigms-grid-based and topological-, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency.
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Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking
TL;DR: A taxonomy is provided and the state of the art in IoT security research is surveyed, and a roadmap of concrete research challenges related to the application of ML and SDN to address existing and next-generation IoT security threats is offered.
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 *.