Markov Decision Processes (MDPs) are mathematical frameworks used to model decision-making situations where outcomes are partly random and partly under the control of a decision-maker. They consist of states, actions, transition probabilities, and rewards, allowing for the analysis of optimal strategies over time. MDPs are essential in dynamic programming applications because they provide a structured way to evaluate the long-term consequences of decisions in uncertain environments.
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