As time goes by, and given enough iterations, it’ll figure out the expert strategy of sitting down on cue. At some point, it accidentally lands on its butt and gets a sudden reward. At first the dog is clueless and tries random things on your command.

This is analogous to teaching a dog to sit down using treats. Luckily, all you need is a reward mechanism, and the reinforcement learning model will figure out how to maximize the reward, if you just let it “play” long enough. After all, not even Lee Sedol knows how to beat himself in Go. On a high level, you know WHAT you want, but not really HOW to get there. Reinforcement learning is useful when you have no training data or specific enough expertise about the problem. During this series, you will not only learn how to train your model, but also what is the best workflow for training it in the cloud with full version control using the Valohai deep learning management platform. We will start with some theory and then move on to more practical things in the next part.

This is the first part of a tutorial series about reinforcement learning. Reinforcement Learning Tutorial Part 1: Q-Learning Juha Kiili

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