Embedding a state space model into a Markov decision process

Embedding a state space model into a Markov decision process

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Article ID: iaor20119928
Volume: 190
Issue: 1
Start Page Number: 289
End Page Number: 309
Publication Date: Oct 2011
Journal: Annals of Operations Research
Authors: , ,
Keywords: programming: markov decision
Abstract:

In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different ways of discretizing an SSM are discussed and methods for reducing the state space of the MDP are presented. An example from dairy production is given.

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