Partially observable markov decision process

2011
In many applications the decision maker has only partial information about the state process, i.e. part of the state cannot be observed. Examples can be found in engineering, economics, statistics, speech recognition and learning theory among others. An important financial application is given when the drift of a stock price process is unobservable and
Nicole Bäuerle, Ulrich Rieder
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Partially Observable Markov Decision Processes

2020
This chapter covers Partially Observable Markov Decision Processes (POMDPs), that extend MDPs for when the state is not completely observable. After a general introduction to POMDPs, their formal representation and properties are described. The representation of the value function as a set of linear equations (\(\alpha -vectors\)) is presented via a ...
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Partially observed Markov decision processes with binomial observations

Operations Research Letters, 2013
Abstract We consider partially observed Markov decision processes with control limits. We analytically show how the finite-horizon control limits are non-monotonic in (a) the time remaining and (b) the probability of obtaining a conforming unit. We also prove that the infinite-horizon control limit can be calculated by solving a finite set of linear ...
Tal Ben-Zvi, Abraham Grosfeld-Nir
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markov and semi-markov decision processes
deep reinforcement learning
dynamic programming

pomdp
markov decision process
fos: computer and information sciences

reinforcement learning
artificial intelligence cs.ai
partially observable markov decision processes