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This article provides a detailed introduction to dynamic programming (DP), the Bellman equation, and fixed-point methods. It covers core DP algorithms such as value iteration, policy iteration, and backward induction, including both deterministic and stochastic (MDP) settings, with illustrative examples and pseudocode. It highlights how contraction principles guarantee existence and computability of optimal value functions and policies useful for AI and RL contexts.