Dynamic programming is a framework for deriving optimal decision strategies in evolving and uncertain environments. Topics include the principle of optimality in deterministic and stochastic settings, value and policy iteration, connections to Pontryagin maximum principle, imperfect state measurement problems, and simulation-based methods such as online reinforcement learning.