| BRÁZDIL, Tomáš, Ezio BARTOCCI, Dimitrios MILIOS, Guido SANGUINETTI and Luca BORTOLUSSI. Policy learning in continuous-time Markov decision processes using Gaussian Processes. Performance Evaluation. 2017, vol. 116, No 1, p. 84-100. ISSN 0166-5316. Available from: https://dx.doi.org/10.1016/j.peva.2017.08.007. |
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@article{1564396,
author = {Brázdil, Tomáš and Bartocci, Ezio and Milios, Dimitrios and Sanguinetti, Guido and Bortolussi, Luca},
article_number = {1},
doi = {http://dx.doi.org/10.1016/j.peva.2017.08.007},
keywords = {continuous-time Markov decision processes; reachability; gradient descent},
language = {eng},
issn = {0166-5316},
journal = {Performance Evaluation},
title = {Policy learning in continuous-time Markov decision processes using Gaussian Processes},
url = {http://dx.doi.org/10.1016/j.peva.2017.08.007},
volume = {116},
year = {2017}
}
TY - JOUR
ID - 1564396
AU - Brázdil, Tomáš - Bartocci, Ezio - Milios, Dimitrios - Sanguinetti, Guido - Bortolussi, Luca
PY - 2017
TI - Policy learning in continuous-time Markov decision processes using Gaussian Processes
JF - Performance Evaluation
VL - 116
IS - 1
SP - 84-100
EP - 84-100
SN - 01665316
KW - continuous-time Markov decision processes
KW - reachability
KW - gradient descent
UR - http://dx.doi.org/10.1016/j.peva.2017.08.007
L2 - http://dx.doi.org/10.1016/j.peva.2017.08.007
N2 - Continuous-time Markov decision processes provide a very powerful mathematical framework to solve policy-making problems in a wide range of applications, ranging from the control of populations to cyber–physical systems. The key problem to solve for these models is to efficiently compute an optimal policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we introduce a novel method based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. Our approach presents several advantages over the classical methods based on discretisation techniques, as it does not assume the a-priori knowledge of a model that can be replaced by a black-box, and does not suffer from state-space explosion. The use of a stochastic moment-based gradient ascent algorithm to guide our search considerably improves the efficiency of learning policies and accelerates the convergence using the momentum term. We demonstrate the strong performance of our approach on two examples of non-linear population models: an epidemiology model with no permanent recovery and a queuing system with non-deterministic choice.
ER -
BRÁZDIL, Tomáš, Ezio BARTOCCI, Dimitrios MILIOS, Guido SANGUINETTI and Luca BORTOLUSSI. Policy learning in continuous-time Markov decision processes using Gaussian Processes. \textit{Performance Evaluation}. 2017, vol.~116, No~1, p.~84-100. ISSN~0166-5316. Available from: https://dx.doi.org/10.1016/j.peva.2017.08.007.
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