Markov decision process

Known as: Value iteration, Policy iteration, Markov decision problems 
Markov decision processes (MDPs) provide a mathematical framework for modeling decision making in situations where outcomes are partly random and… (More)
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Papers overview

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Review
2017
Review
2017
Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the… (More)
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Highly Cited
2012
Highly Cited
2012
We explain the need for safe exploration methods in Markov Decision Processes and present three different formulations of safety… (More)
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Highly Cited
2005
Highly Cited
2005
Optimal solutions to Markov decision problems may be very sensitive with respect to the state transition probabilities. In many… (More)
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Highly Cited
2003
Highly Cited
2003
There has been substantial progress with formal models for sequential decision making by individual agents using the Markov… (More)
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Highly Cited
2000
Highly Cited
2000
The theory of Markov Decision Processes is the theory of controlled Markov chains. Its origins can be traced back to R. Bellman… (More)
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Highly Cited
2000
Highly Cited
2000
The bidding decision making problem is studied from a supplier’s viewpoint in a spot market environment. The decision-making… (More)
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Highly Cited
1999
Highly Cited
1999
A critical issue for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning… (More)
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Highly Cited
1998
Highly Cited
1998
In this paper we introduce a stochastic model for dialogue systems based on Markov decision process. Within this framework we… (More)
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Highly Cited
1998
Highly Cited
1998
This dissertation investigates the use of hierarchy and problem decomposition as a means of solving large, stochastic, sequential… (More)
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Highly Cited
1997
Highly Cited
1997
We use the notion of stochastic bisimulation homo-geneity to analyze planning problems represented as Markov decision processes… (More)
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