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Algebraic Markov Decision Processes
, 2005 In this paper, we provide an algebraic approach to Markov Decision Processes (MDPs), which allows a unified treatment of MDPs and includes many existing models (quantitative or qualitative) as particular cases. In algebraic MDPs, rewards are expressed in a semiring structure, uncertainty is represented by a decomposable plausibility measure valued on a
Perny, Patrice +2 more
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A Weighted Markov Decision Process [PDF]
Operations Research, 1992 The two most commonly considered reward criteria for Markov decision processes are the discounted reward and the long-term average reward. The first tends to “neglect” the future, concentrating on the short-term rewards, while the second one tends to do the opposite.
Krass, D, Filar, JA, Sinha, SS
openaire +4 more sources
Discounted Markov Decision Processes with Constrained Costs: the decomposition approach [PDF]
E3S Web of Conferences, 2021 In this paper we consider a constrained optimization of discrete time Markov Decision Processes (MDPs) with finite state and action spaces, which accumulate both a reward and costs at each decision epoch.
Semmouri Abdellatif +2 more
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Multi-Objective Model Checking of Markov Decision Processes [PDF]
Logical Methods in Computer Science, 2008 We study and provide efficient algorithms for multi-objective model checking problems for Markov Decision Processes (MDPs). Given an MDP, M, and given multiple linear-time (\omega -regular or LTL) properties \varphi\_i, and probabilities r\_i \epsilon [0,
Kousha Etessami +3 more
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Multi-weighted Markov Decision Processes with Reachability Objectives [PDF]
Electronic Proceedings in Theoretical Computer Science, 2018 In this paper, we are interested in the synthesis of schedulers in double-weighted Markov decision processes, which satisfy both a percentile constraint over a weighted reachability condition, and a quantitative constraint on the expected value of a ...
Patricia Bouyer +3 more
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Debugging of Markov Decision Processes (MDPs) Models [PDF]
Electronic Proceedings in Theoretical Computer Science, 2016 In model checking, a counterexample is considered as a valuable tool for debugging. In Probabilistic Model Checking (PMC), counterexample generation has a quantitative aspect. The counterexample in PMC is a set of paths in which a path formula holds, and
Hichem Debbi
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Learning Markov Decision Processes for Model Checking [PDF]
Electronic Proceedings in Theoretical Computer Science, 2012 Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system ...
Hua Mao +5 more
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Of Cores: A Partial-Exploration Framework for Markov Decision Processes [PDF]
Logical Methods in Computer Science, 2020 We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a "core" of an MDP, i.e., a subsystem where we provably remain with high ...
Jan Křetínský, Tobias Meggendorfer
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Framework for solving time-delayed Markov Decision Processes
Physical Review Research, 2023 Reinforcement learning has revolutionized our understanding of evolved systems and our ability to engineer systems based on a theoretical framework for understanding how to maximize expected reward. However, time delays between the observation and action
Yorgo Sawaya +2 more
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Finite State Approximations for Countable State Infinite Horizon Discounted Markov Decision Processes [PDF]
Modeling, Identification and Control, 1987 It is proved that the optimal policy of a Markov decision process where the state space is truncated, will approximate the policy in case of no truncation.
Sjur D. Flåm
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