# Markovian Decision Rule

A Markovian Decision Rule is a Decision Rule that determines what action to take based on former decisions history for all possible states.

**AKA:**Randomized Decision Rule.**See:**Decision Rule, Markov Decision Process, Decision Epoch, Randomized Experiment.

## References

### 2017

- (Sammut & Webb, 2017) ⇒ Claude Sammut, and Geoffrey I. Webb. (2017). "Markovian Decision Rule”. In: (Sammut & Webb, 2017).
- QUOTE: In a Markov decision process, a decision rule, [math]d_t[/math], 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 [math]A(s)[/math] with probability 1 for each [math]s \in S[/math] and for a given [math]h_t[/math], and it is randomized if it selects a member of [math]A(s)[/math] at random with probability [math] q_{d_t(h_t)} (a)[/math]