Markov Decision
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
... Despite the sustained interest in model-based BRL, the deployment to real-world applications is limited both by scalability and representation issues. In terms of representation, an important chal- lenge for many ...
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Strategy iteration algorithms for games and Markov decision processes
... In this chapter we have shown how Friedmann’s lower bounds for the greedy switch- ing policy in the strategy improvement setting can be extended to apply to the Markov decision process setting. We have ...
226
A hemimetric extension of simulation for semi-markov decision processes
... [7] Jos´ ee Desharnais, Vineet Gupta, Radha Jagadeesan, and Prakash Panangaden. Metrics for labelled Markov processes. Theor. Comput. Sci., 318(3):323–354, 2004. [8] Norm Ferns, Prakash Panangaden, and Doina ...
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Variance Optimization for Continuous Time Markov Decision Processes
... continuous-time Markov decision process ...traditional Markov decision process, the cost function in the variance criterion will be affected by future ...
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Reachability-based model reduction for Markov decision process
... One of the biggest challenges in the probabilistic plan- ning is to solve large Markov decision processes (MDPs) [1]. This is because the number of states in an MDP grows exponentially with the number of ...
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Policy-Gradient Algorithms for Partially Observable Markov Decision Processes
... Adaptive history methods grow or shrink the number of past events that are needed to reveal the hidden state. Probabilistic Suffix Automata [Ron et al., 1994] model partially observable Markov decision ...
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Markov Decision Process based Switching for Wireless Sensor Network
... [2] Sabyasachi Mukhopadhyaya, Debadatta Dashb, Asish Mitrac,Paritosh Bhattacharyad “A comparative study between seasonal wind speed by Fourier and Wavelet analysis” National Institute of Technology Agartala, ...
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Randomized and Relaxed Strategies in Continuous-Time Markov Decision Processes
... Abstract. One of the goals of this article is to describe a wide class of control strategies, which includes the traditional relaxed strategies, as well as the so called randomized strategies which appeared earlier only ...
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Investigation of Computational Reduction Strategies for Markov Decision Processes.
... Bellman[Bel57] first proposed the Markov decision process (MDP) problem. Howard[How60] pre- sented the value iteration method and the policy iteration method to solve the MDP problem, which laid the ...
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Approximate Newton Methods for Policy Search in Markov Decision Processes
... An avenue of research that has received less attention is the application of Newton’s method to Markov decision processes. Although such an extension of the GPOMDP algo- rithm is provided in the work of ...
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A Markov Decision Model for Hospital Ward Admission Scheduling
... horizon Markov decision process model is developed and analyzed for the hospital ward allocation ...a Markov decision process approach, the states of a Markov chain represent possible ...
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Adaptive Markov decision control of high-frequency drip irrigation systems
... University of Southern Queensland Faculty of Health, Engineering & Sciences Adaptive Markov Decision Control Of High Frequency Drip Irrigation Systems Volume 1 A dissertation submitted by Damian Pecke[.] ...
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Strategy improvement algorithm for singularly perturbed discounted Markov decision processes
... perturbed Markov decision process with the discounted reward ...limit Markov control problem which is the optimization problem that should be solved in case of singular ...limit Markov control ...
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Model-based Bayesian Reinforcement Learning in Factored Markov Decision Process
... on Markov decision process (MDP) or partially observable Markov decision process (POMDP) is an interdisciplinary research area of machine learning, control theory, and operations ...
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Compositional reasoning for weighted Markov decision processes
... The rest of this paper is organised as follows. Section 2 is devoted to an exposition of our model, which we call weighted Markov Decision Processes, wMDPs. These correspond to the diagrams we have been ...
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Simplex Algorithm for Countable state Discounted Markov Decision Processes
... The class of Markov decision processes (MDPs) provides a popular framework which covers a wide variety of sequential decision-making problems. An MDP is classified by its criterion being optimized, ...
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Compositional Reasoning for Markov Decision Processes
... Markov decision processes (MDPs) have long been used to model qualitative aspects of systems in the presence of uncertainty [9, 10, ...of Markov decision ...
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Partially Observable Markov Decision Processes for Prostate Cancer Screening.
... referral decision depend on the patient’s age and PSA history? Surprisingly, there has been very little research on determining optimal decisions related to these ...observable Markov decision ...
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Augmenting Markov Decision Processes with Advising
... (Advice Markov Decision Processes). Advice-MDPs are Markov Decision Processes (MDPs (Puterman 1994)) expanded with advis- ing, for defining situationally-forbidden actions and aug- menting ...
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Multi-Objective Markov Decision Processes for Data-Driven Decision Support
... Multi-Objective Markov Decision Processes for developing sequential decision support systems from ...sequential decision-making data to provide support that is useful to many different ...
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