Let's talk about Markov Decision Process (MDP), a framework used in artificial intelligence to model sequential decision-making in uncertain environments. Our source: https://www.geeksforgeeks.org/machine-learning/markov-decision-process/?utm_source=chatgpt.comIt outlines the five core components of an MDP: states, actions, transition models (which account for uncertainty), rewards, and the policy an agent follows. The text further illustrates these concepts with a grid world example, detailing how an agent navigates to a goal while maximizing rewards and avoiding penalties under stochastic movement. Finally, the source highlights diverse real-world applications of MDPs, including robotics, game strategy, healthcare, traffic navigation, and inventory management, demonstrating their practical significance. Hosted on Acast. See acast.com/privacy for more information.

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