Results 31 to 40 of about 26,957 (159)
Configurable Markov Decision Processes
CoRR, 2018 In many real-world problems, there is the possibility to configure, to a limited extent, some environmental parameters to improve the performance of a learning agent. In this paper, we propose a novel framework, Configurable Markov Decision Processes (Conf-MDPs), to model this new type of interaction with the environment.
Metelli, Alberto Maria +2 more
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Probabilistic opacity for Markov decision processes [PDF]
Information Processing Letters, 2015 Opacity is a generic security property, that has been defined on (non probabilistic) transition systems and later on Markov chains with labels. For a secret predicate, given as a subset of runs, and a function describing the view of an external observer, the value of interest for opacity is a measure of the set of runs disclosing the secret.
Béatrice Bérard +2 more
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Learning Algorithms for Verification of Markov Decision Processes [PDF]
TheoretiCSWe present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of the ...
Tomáš Brázdil +8 more
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Accelerating Iterative Methods for Bounded Reachability Probabilities in Markov Decision Processes [PDF]
Computer and Knowledge Engineering, 2020 Probabilistic model checking is a formal method for verification of the quantitative and qualitative properties of computer systems with stochastic behaviors.
Mohammadsadegh Mohagheghi
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Multi-model Markov decision processes
IISE Transactions, 2021 Markov decision processes (MDPs) have found success in many application areas that involve sequential decision making under uncertainty, including the evaluation and design of treatment and screening protocols for medical decision making. However, the data used to parameterize the model can influence what policies are recommended, and multiple ...
Lauren N. Steimle +2 more
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Foundations of probability-raising causality in Markov decision processes [PDF]
Logical Methods in Computer ScienceThis work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects ...
Christel Baier +2 more
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Reachability in Recursive Markov Decision Processes [PDF]
Information and Computation, 2006 A class of infinite-state Markov decision processes generated by stateless pushdown automata is considered. This class corresponds to 1 1/2-player games over graphs generated by BPA systems or (equivalently) 1-exit recursive state machines. An extended reachability objective is specified by two sets \(S\) and \(T\) of safe and terminal stack ...
Tomás Brázdil +3 more
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The complexity of synchronizing Markov decision processes [PDF]
Journal of Computer and System Sciences, 2019 arXiv admin note: substantial text overlap with arXiv:1402.2840, arXiv:1310 ...
Laurent Doyen 0001 +2 more
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A tutorial introduction to reinforcement learning
SICE Journal of Control, Measurement, and System Integration, 2023 In this paper, we present a brief survey of reinforcement learning, with particular emphasis on stochastic approximation (SA) as a unifying theme. The scope of the paper includes Markov reward processes, Markov decision processes, SA algorithms, and ...
Mathukumalli Vidyasagar
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Markov decision processes in minimization of expected costs
Croatian Operational Research Review, 2014 Basics of Markov decision processes will be introduced in order to obtain the optimization goal function for minimizing the long-run expected cost. We focus on mini-mization of such cost of the farmer's policy consisting of different decisions in speci c
Marija Rukav +3 more
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