Definition
A Markov decision process is a 4-tuple, where
- is a finite set of states,
- is a finite set of actions (alternatively, is the finite set of actions available from state ),
- is the probability that action in state at time will lead to state at time ,
- is the immediate reward (or expected immediate reward) received after transition to state from state with transition probability .
(The theory of Markov decision processes does not actually require or to be finite, but the basic algorithms below assume that they are finite.)
Read more about this topic: Markov Decision Process
Famous quotes containing the word definition:
“... if, as women, we accept a philosophy of history that asserts that women are by definition assimilated into the male universal, that we can understand our past through a male lens—if we are unaware that women even have a history—we live our lives similarly unanchored, drifting in response to a veering wind of myth and bias.”
—Adrienne Rich (b. 1929)
“Perhaps the best definition of progress would be the continuing efforts of men and women to narrow the gap between the convenience of the powers that be and the unwritten charter.”
—Nadine Gordimer (b. 1923)
“Beauty, like all other qualities presented to human experience, is relative; and the definition of it becomes unmeaning and useless in proportion to its abstractness. To define beauty not in the most abstract, but in the most concrete terms possible, not to find a universal formula for it, but the formula which expresses most adequately this or that special manifestation of it, is the aim of the true student of aesthetics.”
—Walter Pater (1839–1894)