Results 11 to 20 of about 57,303 (303)
A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process. [PDF]
Pharmacoeconomics, 2022 The most appropriate next step in depression treatment after the initial treatment fails is unclear. This study explores the suitability of the Markov decision process for optimizing sequential treatment decisions for depression.
Li F, Jörg F, Li X, Feenstra T.
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Variance-penalized Markov decision process
Mathematics of Operations Research, 1989 We consider a Markov decision process with both the expected limiting average, and the discounted total return criteria, appropriately modified to include a penalty for the variability in the stream of rewards.
Filar, Jerzy A. +2 more
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Quantile Markov Decision Processes
Operations Research, 2022 Title: Sequential Decision Making Using Quantiles The goal of a traditional Markov decision process (MDP) is to maximize the expectation of cumulative reward over a finite or infinite horizon. In many applications, however, a decision maker may be interested in optimizing a specific quantile of the cumulative reward. For example, a physician may want
Xiaocheng Li +2 more
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A Weighted Markov Decision Process [PDF]
Operations Research, 1992 The two most commonly considered reward criteria for Markov decision processes are the discounted reward and the long-term average reward. The first tends to “neglect” the future, concentrating on the short-term rewards, while the second one tends to do the opposite.
Krass, D, Filar, JA, Sinha, SS
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Quantum logic gate synthesis as a Markov decision process
npj Quantum Information, 2023 Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov decision processes (MDPs).
M. Sohaib Alam +2 more
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Machine Learning and Knowledge Extraction, 2021 The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo, Huanyu Zang
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Machine Learning and Knowledge Extraction, 2021 The first part of a two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (DRL) applications for solving partially observable Markov decision processes (POMDP) problems.
Xuanchen Xiang, Simon Foo
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The Complexity of Markov Decision Processes [PDF]
Mathematics of Operations Research, 1987 We investigate the complexity of the classical problem of optimal policy computation in Markov decision processes. All three variants of the problem (finite horizon, infinite horizon discounted, and infinite horizon average cost) were known to be solvable in polynomial time by dynamic programming (finite horizon problems), linear programming, or ...
Christos H. Papadimitriou +1 more
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Robust Markov Decision Processes [PDF]
Mathematics of Operations Research, 2013 Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use because of their sensitivity to distributional model parameters, which are typically unknown and have to be estimated by the decision maker.
Wolfram Wiesemann +2 more
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Health Status-Based Predictive Maintenance Decision-Making via LSTM and Markov Decision Process
Mathematics, 2022 Maintenance decision-making is essential to achieve safe and reliable operation with high performance for equipment. To avoid unexpected shutdown and increase machine life as well as system efficiency, it is fundamental to design an effective maintenance
Pan Zheng +4 more
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