Random-horizon Markov decision processes with fuzzy rewards on Borel spaces
Open MathematicsWe study a class of Markov decision processes on Borel state spaces motivated by situations where both the horizon and the notion of performance are uncertain.
Cruz-Suárez Hugo +2 more
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Nonuniqueness versus Uniqueness of Optimal Policies in Convex Discounted Markov Decision Processes
Journal of Applied Mathematics, 2013 From the classical point of view, it is important to determine if in a Markov decision process (MDP), besides their existence, the uniqueness of the optimal policies is guaranteed.
Raúl Montes-de-Oca +2 more
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BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES. [PDF]
Ann Stat, 2022 Liao P +4 more
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Partially Observable Markov Decision Processes in Shared Autonomy Applications: A Survey
IEEE AccessShared autonomy consists of a collaborative effort between a human user and a robotic system having a shared goal, in which the human-controlled robot adapts its behaviour to provide assistive actions.
Shyrailym Shaldambayeva +4 more
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Policy Iteration for Continuous-Time Average Reward Markov Decision Processes in Polish Spaces
Abstract and Applied Analysis, 2009 We study the policy iteration algorithm (PIA) for continuous-time jump Markov decision processes in general state and action spaces. The corresponding transition rates are allowed to be unbounded, and the reward rates may have neither upper nor lower ...
Quanxin Zhu +2 more
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Control Design for Untimed Petri Nets Using Markov Decision Processes
Operations Research and Decisions, 2017 Design of control sequences for discrete event systems (DESs) has been presented modelled by untimed Petri nets (PNs). PNs are well-known mathematical and graphical models that are widely used to describe distributed DESs, including choices ...
Cherki Daoui, Dimitri Lefebvre
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Fuel in Markov Decision Processes (FiMDP): A Practical Approach to Consumption. [PDF]
FME, 2021 Blahoudek F +5 more
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On incorporating forecasts into linear state space model Markov decision processes. [PDF]
Philos Trans A Math Phys Eng Sci, 2021 de Chalendar JA, Glynn PW.
europepmc +1 more source
Learning parametric policies and transition probability models of markov decision processes from data. [PDF]
Eur J Control, 2021 Xu T, Zhu H, Paschalidis IC.
europepmc +1 more source
Differentially Private Reward Functions in Markov Decision Processes: Policy Synthesis and Tradeoffs
IEEE Open Journal of Control SystemsPolicy synthesis in Markov decision processes uses a known reward function to compute a policy that maximizes it. However, onlookers may infer reward functions by observing agents, which can reveal sensitive information.
Alexander Benvenuti +6 more
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