Results 141 to 150 of about 81,989 (164)

Semi-Markov decision processes with variance minimization criterion

4OR, 2014
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Wei, Qingda, Guo, Xianping
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Time-average optimal constrained semi-Markov decision processes

Advances in Applied Probability, 1986
Optimal causal policies maximizing the time-average reward over a semi-Markov decision process (SMDP), subject to a hard constraint on a time-average cost, are considered. Rewards and costs depend on the state and action, and contain running as well as switching components. It is supposed that the state space of the SMDP is finite, and the action space
Beutler, Frederick J., Ross, Keith W.
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Semi-Markov Decision Process With Partial Information for Maintenance Decisions

IEEE Transactions on Reliability, 2014
A critical factor that prevents optimal scheduling of maintenance interventions is the uncertainty regarding the current condition of the asset under consideration, as well as the rate at which deterioration takes place. However, current maintenance modeling and optimization techniques assume that the condition of the asset is either known, or assumed ...
Rengarajan Srinivasan, Ajith Kumar Parlikad   +1 more
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Mean-Variance Problems for Finite Horizon Semi-Markov Decision Processes

Applied Mathematics & Optimization, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Huang, Yonghui, Guo, Xianping
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Constrained Semi-Markov decision processes with average rewards

ZOR Zeitschrift f�r Operations Research Mathematical Methods of Opeartions Research, 1994
This paper deals with constrained average reward semi-Markov decision processes with finite state and action sets. Two average reward criteria are considered, namely time average and ratio average. The author proved the existence of optimal mixed stationary policies and showed, under the unichain condition, the existence of randomized stationary ...
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Semi-Markov decision processes with polynomial reward

Journal of Applied Probability, 1982
A 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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Discrete-time equivalence for constrained semi-Markov decision processes

1985 24th IEEE Conference on Decision and Control, 1985
A continuous-time average reward Markov decision process problem is most easily solved in terms of an equivalent discrete-time Markov decision process (DMDP); customary hypotheses include that the process is a Markov jump process with denumerable state space and bounded transition rates, that actions are chosen at the jump points of the process, and ...
Frederick Beutler, Keith Ross
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Constrained Discounted Semi-Markov Decision Processes

2002
This 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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Uniformization for semi-Markov decision processes under stationary policies

Journal of Applied Probability, 1987
Uniformization permits the replacement of a semi-Markov decision process (SMDP) by a Markov chain exhibiting the same average rewards for simple (non-randomized) policies. It is shown that various anomalies may occur, especially for stationary (randomized) policies; uniformization introduces virtual jumps with concomitant action changes not present in ...
Beutler, Frederick J., Ross, Keith W.
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Reinforcement learning for semi-Markov decision processes with applications

2023
This 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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