The Markov Decision Process (MDP) forms the basis of setting up RL, where the outcome of a decision is semi-controlled; that is, it is partly random and partly controlled (by the decision-maker). An MDP is defined using a set of possible states (S), a set of possible actions (A), a real-values reward function (R), and a set of transition probabilities from one state to another state for a given action (T). In addition, the effects of an action performed on one state depends only on that state and not on its previous states.