Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment to achieve a certain goal. At the heart of reinforcement learning lies the concept of Markov Decision Processes (MDPs), which provide a mathematical framework for modeling decision-making situations where outcomes are partly random and partly under the control of the decision-maker.

An MDP consists of a set of states representing different situations the agent might encounter, a set of actions the agent can take, a reward function that provides feedback on the success of the agent’s actions, and a transition model that defines how actions taken in a given state lead to subsequent states. The goal of the agent is to find a policy—a strategy that defines the best action to take in each state—that maximizes the cumulative reward over time. MDPs are central to reinforcement learning as they provide a structured approach to learning optimal behaviors through trial and error, balancing the exploration of new strategies with the exploitation of known, rewarding actions.