Markov decision processes
Markov Decision Processes
... • Need to define states, actions, rewards, and dynamics 52 Chapter 3: Finite Markov Decision Processes Example 3.3 Recycling Robot A mobile robot has the job of collecting empty soda cans in an ...
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Sufficient Markov Decision Processes.
... Sufficient Markov Decision ...data-driven decision making include autonomous vehicles, intelligent power grids, and precision medicine through mobile ...health. Markov decision ...
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Configurable Markov Decision Processes
... both Markov decision processes with imprecise probabilities and non- stationary Markov decision processes do not admit the pos- sibility to dynamically alter the environmental ...
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Risk-sensitive Markov Decision Processes
... time Markov decision processes, where the system performance is measured by the expectation of the exponential utility of the total ...time Markov decision process, whose action space ...
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One-Counter Markov Decision Processes
... Recursive Markov Decision Processes (RMDPs) was studied in [10, ...branching processes, stochastic context-free grammars, and a related model called pBPAs), [10] showed quantitative problems ...
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Scalable Verification of Markov Decision Processes
... Introduction Markov decision processes (MDP) describe systems that interleave nondetermin- istic actions and probabilistic transitions, possibly with rewards or costs assigned to the actions ...
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Compositional Reasoning for Markov Decision Processes
... Abstract. Markov decision processes (MDPs) have long been used to model qualitative aspects of systems in the presence of uncertainty. However, much of the literature on MDPs takes a monolithic ...
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Hedging Bets in Markov Decision Processes
... of Markov decision processes with costs or rewards, while widely used to formalize optimal decision making, cannot capture scenarios where there are multiple objectives for the agent during ...
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Markov decision processes with uncertain parameters
... Theoretical results In the beginning, we pursued the theoretical implications of uncer- tainty in Markov decision processes, including the aforementioned models, but also para- metric formalisms and ...
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Augmenting Markov Decision Processes with Advising
... (Advice Markov Decision Processes). Advice-MDPs are Markov Decision Processes (MDPs (Puterman 1994)) expanded with advis- ing, for defining situationally-forbidden actions and ...
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Multiple-Environment Markov Decision Processes
... Interval Markov chains are Markov chains in which transition probabilities are only known to belong to given intervals (see ...Similarly, Markov decision processes with uncertain ...
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Solving Hybrid Markov Decision Processes
... Abstract. Markov decision processes (MDPs) have developed as a stan- dard for representing uncertainty in decision-theoretic planning. How- ever, MDPs require an explicit representation of the ...
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Bounded-parameter Markov decision processes
... 8. Related work and conclusions Our definition for bounded-parameter MDPs is related to a number of other ideas appearing in the literature on Markov decision processes; in the following, we mention ...
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Structural Results for Constrained Markov Decision Processes
... INTRODUCTION Markov Decision Processes (MDPs) have proven to be a useful tool in modelling the dynamic control of service ...inform decision-makers of the class of policies they should ...
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Compositional reasoning for weighted Markov decision processes
... We have proposed a model of weighted Markov decision processes, wMDP, for compositional reasoning about the behaviour of systems with uncertainty. Amortised weighted simulation is coinductively ...
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On the Complexity of Reachability in Parametric Markov Decision Processes
... Markov decision processes (MDPs) are the model to reason about sequential processes under (stochastic) uncertainty and ...non-determinism. Markov chains (MCs) are MDPs without non- ...
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Markov Decision Processes for Ad Network Optimization
... sequential decision making; that is, a planning ...on Markov Decision Processes, and by resorting to plan generation techniques that have been developed in the AI ...
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Markov Decision Processes and the Modelling of Patient Flows
... as Markov chains on a week-to- week basis, and can be formulated as Markov Decision Processes with the addition of actions and associated ...These decision processes can be used ...
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Quantum partially observable Markov decision processes
... One motivation for studying QOMDPs is simply that they are the natural quantum generalizations of POMDPs, which are central objects of study in artificial intelligence. Moreover, as we show here, QOMDPs have different ...
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Trading performance for stability in Markov decision processes
... © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). 1. Introduction Markov decision processes (MDPs) ...
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