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Welcome back to this series on reinforcement learning! In this video, we'll be introducing the idea of Q-learning with value iteration, which is a reinforcement learning technique used for learning the optimal policy in a Markov Decision Process.

We'll illustrate how this technique works by introducing a game where a reinforcement learning agent tries to maximize points, and through this, we'll also learn about Q-tables and the trade-off between exploration and exploitation.

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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