Markov decision processes (MDPs)
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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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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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
... Markov decision processes (MDP) are a standard formalism for modeling systems that ex- hibit both stochastic and non-deterministic aspects. At each round of the execution of an MDP, an action is ...
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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
... Weighted Markov decision processes (MDPs) have long been used to model quantitative aspects of systems in the presence of uncertainty. However, much of the literature on such MDPs takes a monolithic ...
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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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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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Constrained Risk-Averse Markov Decision Processes
... for Markov decision processes (MDPs) with dynamic co- herent risk objectives and ...a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies ...
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Quantum partially observable Markov decision processes
... a decision problem (namely, goal-state reachability) that is computable for POMDPs but uncomputable for ...quantum Markov decision processes (MDPs) and proved undecidability results for them ...
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Multi-Objective Markov Decision Processes for Data-Driven Decision Support
... sequential decision support problems are not modelled well by ...medical decision will be based not only on the effec- tiveness of a treatment, but also on its potential side-effects, cost, and other ...
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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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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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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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