RL has been applied to a wide variety of fields, including robotics, finance, healthcare, and intelligent transportation systems. In general, they can be grouped into three major areas—automatic machines (such as autonomous vehicles, smart grids, and robotics), optimization processes (for example, planned maintenance, supply chains, and process planning) and control (for example, fault detection and quality control). 

In the beginning, RL was only ever applied to simple problems, but deep RL opened the road to different problems, making it possible to deal with more complex tasks. Nowadays, deep RL has been showing some very promising results. Unfortunately, many of these breakthroughs are limited to research applications or games, and, in many situations, it is not easy to bridge the gap between purely research-oriented applications and industry problems. Despite this, more companies are moving toward the adoption of RL in their industries and products.

We will now take a look at the principal fields that are already adopting or will benefit from RL.