In a Markov Decision Process (MDP), the state space defines all possible states the system can be in, representing the environment's configuration at any given time. The action space encompasses all possible actions an agent can take, dictating how the agent can interact with the environment to transition between states. The reward function maps state-action pairs (or state-action-next state triplets) to numerical rewards, quantifying the immediate feedback the agent receives for performing an action in a particular state. Together, these elements, along with the transition dynamics and a discount factor, enable the formulation of sequential decision-making problems where the agent aims to maximize cumulative rewards over time.