Reinforcement Learning is an exciting field of Machine Learning that’s attracting a lot of attention and popularity. An important reason for this popularity is due to breakthroughs in Reinforcement Learning where computer algorithms such as Alpha Go and OpenAI Five have been able to achieve human level performance on games such as Go and Dota 2. One of the core concepts in Reinforcement Learning is the Deep Q-Learning algorithm . Naturally, a lot of us want to learn more about the algorithms behind these impressive accomplishments. In this tutorial, we’ll be sharing a minimal Deep Q-Network implementation (minDQN) meant as a practical guide to help new learners code their own Deep Q-Networks.

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