Markov Decision Processes (MDPs) are mathematical frameworks used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. They provide a structured way to represent environments where an agent makes a series of choices over time, taking into account the probabilities of different outcomes and the associated rewards. MDPs are fundamental in reinforcement learning as they help define the environment in which an agent learns to make decisions based on its experiences.
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