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

Markov Decision Processes (MDPs) provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and involve a sequence of decisions. An MDP encompasses states, actions, transition probabilities, and rewards, which collectively define the decision-making scenario.

In this approach, the decision-maker (often modeled as an agent) must make decisions at each state while considering both the randomness in the outcomes of their actions and the long-term rewards associated with state transitions. This is particularly useful in environments where uncertainty is a significant factor, allowing the agent to evaluate the expected utility of different strategies and choose actions that maximize their long-term rewards.

MDPs are widely used in various fields, including artificial intelligence, operations research, economics, and robotics, where systems operate under uncertainty and require optimal decision-making processes over time.