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.)
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