The Markov Decision Process is the formal description of the Reinforcement Learning problem. It includes concepts like states, actions, rewards, and how an agent makes decisions based on a given policy. So, what Reinforcement Learning algorithms do is to find optimal solutions to Markov Decision Processes.

Markov Decision Process

Because it is a fundamental concept in the Reinforcement Learning domain, we selected more than 40 resources about Markov Decision Process, including blog posts, books, and videos. Check the links below.

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