Introduction to Reinforcement Learning with Python



Reinforcement Learning is definitely one of the most active and stimulating areas of research in AI.

The interest in this field grew exponentially over the last couple of years, following great (and greatly publicized) advances, such as DeepMind’s AlphaGo beating the word champion of GO, and OpenAI AI models beating professional DOTA players.

Thanks to all of these advances, Reinforcement Learning is now being applied in a variety of different fields, from healthcare to finance, from chemistry to resource management.

In this article, we will introduce the fundamental concepts and terminology of Reinforcement Learning, and we will apply them in a practical example.

What is Reinforcement Learning?

Reinforcement Learning (RL) is a branch of machine learning concerned with actors, or agents, taking actions is some kind of environment in order to maximize some type of reward that they collect along the way.

This is deliberately a very loose definition, which is why reinforcement learning techniques can be applied to a very wide range of real-world problems.

Imagine someone playing a video game. The player is the agent, and the game is the environment. The rewards the player

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