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In a Markov decision process, a decision rule, d t , determines what action to take, based on the history to date at a given decision epoch and for any possible state. It is deterministic if it selects a single member of A(s) with probability 1 for each s ∈ S and for a given h t , and it is randomized if it selects a member of A(s) at random with probability \(q_{d_{t}(h_{t})}(a)\). It is Markovian if it depends on h t only through s t . That is, \(d_{t}(h_{t}) = d_{t}(s_{t})\).
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(2017). Markovian Decision Rule. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_518
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_518
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Publisher Name: Springer, Boston, MA
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