MDP stands for “Markov Decision Process.” It is a mathematical framework used in the field of artificial intelligence and reinforcement learning to model decision-making problems. MDPs are a way to represent situations where an agent, which could be a robot, a computer program, or any decision-maker, interacts with an environment to achieve specific goals.
In an MDP, the decision-making process is characterized by the following key elements:
States: These represent the different situations or configurations in which the agent can find itself. States define the context in which decisions are made.
Actions: These are the choices available to the agent to influence the environment or transition from one state to another.
Transitions: MDPs specify how the system moves from one state to another based on the chosen actions. This transition is probabilistic and governed by transition probabilities.
Rewards: At each state transition, the agent receives a numerical reward or penalty, which quantifies the desirability of the transition. The agent’s objective is typically to maximize the expected cumulative reward over time.
Policies: A policy is a strategy or a rule that defines which actions the agent should take in each state to achieve its goals.
MDPs provide a formal framework for solving decision-making problems by finding optimal policies that maximize expected rewards. They are used in various applications, including robotics, game playing, autonomous vehicles, and many other fields where intelligent decision-making is required.