# Introduction to Markov Decision Process (MDP)

Learn the basics of Markov's decision process and its use in commercial games.

## We'll cover the following

## The approach

Markov decision processes (MDPs) are a way to formulate problems that are characterized by actions to maximize rewards in a fully observable situation or environment. An MDP consists of a set of world states, $S$, and available actions from those states, $A$. The MDP may be probabilistic in that the actual result of taking an action (the next state

An MDP is commonly used as a problem formulation in reinforcement learning because an optimal policy for an MDP can be found by repeated simulation and iteration without prior human-labeled data as long as the world state is clearly defined, the action space is limited, and the reward function is known.

Get hands-on with 1200+ tech skills courses.