Artificial Intelligence Programming 2025 – 400 Free Practice Questions to Pass the Exam

A Markov Decision Process (MDP) is indeed defined as a mathematical framework for modeling decision-making situations in which outcomes are influenced by both random factors and the choices made by a decision maker. The key components of an MDP include states, actions, transition probabilities, and rewards, which collectively help to describe scenarios where decisions must be made over time.

The framework efficiently captures the underlying dynamics of a system where the decision maker's actions can lead to various outcomes that are not entirely predictable, allowing for the incorporation of uncertainty into the decision-making process. This makes MDPs essential in fields such as reinforcement learning, robotics, and economics, where agents must learn to make sequences of decisions in uncertain environments.

Understanding the core elements of MDPs is vital for exploring various applications in AI, especially in situations that involve long-term planning and sequential decision-making.