40 Resources to Completely Master Markov Decision Processes

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.

Blog Posts

Books

Videos

Markov Decision Processes (MDPs) – Structuring a Reinforcement Learning Problem
Markov Decision Processes – Georgia Tech – Machine Learning
Markov Decision Processes Two – Georgia Tech – Machine Learning
Markov Decision Processes Three – Georgia Tech – Machine Learning
Markov Decision Processes Four – Georgia Tech – Machine Learning
RL Course by David Silver – Lecture 2: Markov Decision Process
Reinforcement Learning 3: Markov Decision Processes and Dynamic Programming
Lecture 8 MDPs
Lecture 9: Markov Decision Process II
Markov Rewards and Dynamic Programming
Markov Chains I
Markov Chains II
Markov Chains III
Reinforcement Learning and Markov Decision Processes I
Reinforcement Learning and Markov Decision Processes II
Markov Decision Processes
Markov Decision Process (MDP) Tutorial
introduction to Markov Decision Processes (MFD)
An Introduction to Markov Decision Processes and Reinforcement Learning
CS885 Lecture 2a: Markov Decision Processes
Grad Course in AI (#11): Markov Decision Processes
Marcus Hutter – Markov Decision Processes

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