scholarly journals Policy Learning for Time-Bounded Reachability in Continuous-Time Markov Decision Processes via Doubly-Stochastic Gradient Ascent

2016 ◽  
pp. 244-259 ◽  
Author(s):  
Ezio Bartocci ◽  
Luca Bortolussi ◽  
Tomǎš Brázdil ◽  
Dimitrios Milios ◽  
Guido Sanguinetti
Keyword(s):  
Markov Decision Processes ◽  
Continuous Time ◽  
Decision Processes ◽  
Stochastic Gradient ◽  
Policy Learning ◽  
Doubly Stochastic ◽  
Gradient Ascent ◽  
Markov Decision ◽  
Stochastic Gradient Ascent

scholarly journals Policy learning in continuous-time Markov decision processes using Gaussian Processes

2017 ◽  
Vol 116 ◽  
pp. 84-100 ◽  
Author(s):  
Ezio Bartocci ◽  
Luca Bortolussi ◽  
Tomáš Brázdil ◽  
Dimitrios Milios ◽  
Guido Sanguinetti
Keyword(s):  
Markov Decision Processes ◽  
Gaussian Processes ◽  
Continuous Time ◽  
Decision Processes ◽  
Policy Learning ◽  
Markov Decision

scholarly journals Denumerable state continuous time Markov decision processes with unbounded cost and transition rates under average criterion

2002 ◽  
Vol 43 (4) ◽  
pp. 541-557 ◽  
Author(s):  
Xianping Guo ◽  
Weiping Zhu
Keyword(s):  
Markov Decision Processes ◽  
Continuous Time ◽  
Decision Processes ◽  
Transition Rates ◽  
Birth And Death Processes ◽  
Optimality Equation ◽  
Average Criterion ◽  
Markov Decision ◽  
Unbounded Cost ◽  
Queue Model

AbstractIn this paper, we consider denumerable state continuous time Markov decision processes with (possibly unbounded) transition and cost rates under average criterion. We present a set of conditions and prove the existence of both average cost optimal stationary policies and a solution of the average optimality equation under the conditions. The results in this paper are applied to an admission control queue model and controlled birth and death processes.

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