REINFORCEMENT LEARNING

A Practitioner’s Guide to Reinforcement Learning

Take your first steps in writing game-winning AI agents

Photo by Vincent Guth on Unsplash

In machine learning, data scientists primarily navigate the territories of supervised and unsupervised learning. However, there is a distinct and interesting subfield — reinforcement learning!

In reinforcement learning, we try to teach a so-called agent how to navigate the complexities of games, placing it within a simulated environment where it explores strategies, receives rewards for successful moves, and faces penalties for missteps.

The typical reinforcement overview. Image by the author.

One prominent outcome of the field of reinforcement learning is AlphaGo, a model that has beaten the world champions of Go, a game more complex than chess.

The great thing about reinforcement learning is that we do not have to tell the agent how to win. We just need to tell it what winning or losing looks like.

In chess, for example, it’s checkmating the opponent’s king, and that’s the only guidance we…