Published on July 1, 2019 by

Welcome to a reinforcement learning tutorial. In this part, we’re going to focus on Q-Learning.

Q-Learning is a model-free form of machine learning, in the sense that the AI “agent” does not need to know or have a model of the environment that it will be in. The same algorithm can be used across a variety of environments.

For a given environment, everything is broken down into “states” and “actions.” The states are observations and samplings that we pull from the environment, and the actions are the choices the agent has made based on the observation. For the purposes of the rest of this tutorial, we’ll use the context of our environment to exemplify how this works.

Text-based tutorial and sample code:

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