In a Markov Decision Process (MDP), the policy is a mapping from states to actions that defines the decision-making strategy at each state. The policy can be deterministic, where a specific action is chosen for each state, or stochastic, where actions are chosen probabilistically. The goal of policy optimization is to find the policy that maximizes the expected cumulative reward over time, considering the dynamics of the environment defined by state transitions and rewards. This involves balancing exploration of the environment to gather information about possible outcomes and exploitation of known information to improve performance. Optimal policies enable agents to achieve the highest long-term benefit in complex, uncertain environments.