Results 11 to 20 of about 45,379 (157)
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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Quantile Markov Decision Process
CoRR, 2017 The goal of a traditional Markov decision process (MDP) is to maximize expected cumulative reward over a defined horizon (possibly infinite). In many applications, however, a decision maker may be interested in optimizing a specific quantile of the cumulative reward instead of its expectation.
Li, X, Zhong, H, Brandeau, M
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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.
Wiesemann, Wolfram +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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Complexity, 2021 Cognitive searching optimization is a subconscious mental phenomenon in decision making. Aroused by exploiting accessible human action, alleviating inefficient decision and shrinking searching space remain challenges for optimizing the solution space ...
Bingxuan Ren, Tangwen Yin, Shan Fu
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Logistic Markov Decision Processes [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 User modeling in advertising and recommendation has typically focused on myopic predictors of user responses. In this work, we consider the long-term decision problem associated with user interaction. We propose a concise specification of long-term interaction dynamics by combining factored dynamic Bayesian networks with logistic predictors of user ...
Martin Mladenov +5 more
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