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Markov Decision Processes (MDPs) – Structuring a Reinforcement Learning Problem

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Welcome back to this series on reinforcement learning! In this video, we’ll discuss Markov decision processes, or MDPs. Markov decision processes give us a way to formalize sequential decision making. This formalization is the basis for structuring problems that are solved with reinforcement learning.

We will detail the components that make up an MDP, including: the environment, the agent, the states of the environment, the actions the agent can take in the environment, and the rewards that may be given to the agent for its actions.

Sources:
Reinforcement Learning: An Introduction, Second Edition by Richard S. Sutton and Andrew G. Bartow
http://incompleteideas.net/book/RLbook2020.pdf

Playing Atari with Deep Reinforcement Learning by Deep Mind Technologies
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

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