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Optimal threshold probability and expectation in semi-Markov decision processes
Applied Mathematics and Computation, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Masahiko Sakaguchi, Yoshio Ohtsubo
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Optimization for condition-based maintenance with semi-Markov decision process
Reliability Engineering & System Safety, 2005The semi-Markov decision model is a powerful tool in analyzing sequential decision processes with random decision epochs. In this paper, we have built the semi-Markov decision process (SMDP) for the maintenance policy optimization of condition-based preventive maintenance problems, and have presented the approach for joint optimization of inspection ...
Dongyan Chen, Kishor S. Trivedi
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Performance Optimization of Semi-Markov Decision Processes with Discounted-cost Criteria
European Journal of Control, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Baoqun Yin +3 more
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Semi-Markov decision processes with polynomial reward
Journal of Applied Probability, 1982A semi-Markov decision process, with a denumerable multidimensional state space, is considered. At any given state only a finite number of actions can be taken to control the process. The immediate reward earned in one transition period is merely assumed to be bounded by a polynomial and a bound is imposed on a weighted moment of the next state reached
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Reinforcement learning for semi-Markov decision processes with applications
2023This thesis focuses on semi-Markov decision processes and their connection with Reinforcement Learning via Q-learning technique. We start by discussing some general ideas around Machine Learning, Reinforcement Learning and Hierarchical Reinforcement Learning.
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Constrained Discounted Semi-Markov Decision Processes
2002This paper reduces problems on the existence and the finding of optimal policies for multiple criterion discounted SMDPs to similar problems for MDPs. We prove this reduction and illustrate it by extending to SMDPs several results for constrained discounted MDPs.
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Semi-Markov decision processes with a reachable state-subset
Optimization, 1989We consider the problem of minimizing the long-run average expected cost per unit time in a semi-Markov decision process with arbitrary state and action space, Assuming the existence .of a Borel subset of state space called a reachable state-subset, we derive the optimality equation for the unbounded costs.
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On the Optimality Conditions for Semi-Markov Decision Processes
1977The paper presents a recurrence formula for the difference between expected rewards and sojourn times generated by N transitions of a semi-Markov decision process with finite state space. Using the recurrence formula convergence of policy iteration method can be easily verified and also necessary and sufficient optimality conditions for average optimal
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Learning Automaton for Finite Semi-Markov Decision Processes
1983A finite semi-Markov decision process is studied to maximize the expected average reward. The semi-Markov kernel of the process depends on an unknown parameter taking values in a subset [a, b] of ℝS. A controller modelled as a learning automaton updates sequentially the probabilities of generating decisions based on the observed decisions, states, and ...
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Minimum Average Value-at-Risk for Finite Horizon Semi-Markov Decision Processes in Continuous Time
SIAM Journal on Optimization, 2016Yonghui Huang, Xianping Guo
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