Markov Decision Processes


Markov Decision Processes (MDPs) are mathematical models used to model decision-making problems in Artificial Intelligence and Data Science. MDPs are used to model situations where the outcome of an action is uncertain and depends on the current state of the system. MDPs consist of a set of states, a set of actions, a transition function that defines the probability of moving from one state to another after taking an action, a reward function that assigns a value to each state-action pair, and a discount factor that determines the importance of future rewards. The goal of an MDP is to find a policy that maximizes the expected cumulative reward over time. MDPs are used in a variety of applications, including robotics, game theory, finance, and healthcare.


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