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Partially Observable Markov Decision Processes and Performance Sensitivity Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008The sensitivity-based optimization of Markov systems has become an increasingly important area. From the perspective of performance sensitivity analysis, policy-iteration algorithms and gradient estimation methods can be directly obtained for Markov decision processes (MDPs).
Yanjie, Li, Baoqun, Yin, Hongsheng, Xi
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Partially observed Markov decision processes (POMDPs)
2016A POMDP is a controlled HMM. Recall from §2.4 that an HMM consists of an X -state Markov chain { x k } observed via a noisy observation process { y k }. Figure 7.1 displays the schematic setup of a POMDP where the action u k affects the state and/or observation (sensing) process of the HMM.
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Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chong, Edwin K. P. +2 more
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A Non-stationary Infinite Partially-Observable Markov Decision Process
2014Partially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been successfully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space,
Kosmopoulos, Dimitrios I. +1 more
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Some Monotonicity Results for Partially Observed Markov Decision Processes
Operations Research, 1987This paper provides sufficient conditions for the optimal value in a discrete-time, finite, partially observed Markov decision process to be monotone on the space of state probability vectors ordered by likelihood ratios. The paper also presents sufficient conditions for the optimal policy to be monotone in a simple machine replacement problem, and ...
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Complexity Bounds for Deterministic Partially Observed Markov Decision Processes
Annals of Operations ResearchzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cyrille Vessaire +4 more
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Algorithms for partially observable Markov decision processes
2014Partially Observable Markov Decision Process (POMDP) is a general sequential decision-making model where the effects of actions are nondeterministic and only partial information about world states is available. However, finding near optimal solutions for POMDPs is computationally difficult. Value iteration is a standard algorithm for solving POMDPs. It
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Algorithms for partially observable Markov decision processes
2010The thesis develops methods to solve discrete-time finite-state partially observable Markov decision processes. For the infinite horizon problem, only discounted reward case is considered. Several new algorithms for the finite horizon and the infinite horizon problems are developed. For the finite horizon problem, two new algorithms are developed.
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Computationally Feasible Bounds for Partially Observed Markov Decision Processes
Operations Research, 1991A partially observed Markov decision process (POMDP) is a sequential decision problem where information concerning parameters of interest is incomplete, and possible actions include sampling, surveying, or otherwise collecting additional information.
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Molecular imaging in oncology: Current impact and future directions
Ca-A Cancer Journal for Clinicians, 2022Steven P Rowe, Martin G Pomper
exaly