So far, we have only covered what reinforcement learning algorithms can do. To the reader, reinforcement learning may seem like the panacea for all kinds of problems. But why do we not see a ubiquitous application of reinforcement learning algorithms in real-life situations? The reality is that the field has a myriad of shortcomings that hinder commercial adoption.
Why is it necessary to talk about the field's flaws? We think this will help you build a more holistic, less biased view of reinforcement learning. Moreover, understanding the weaknesses of reinforcement learning and machine learning is an important quality of a good machine learning researcher or practitioner. In the following subsections, we will discuss a few of the most important limitations that reinforcement learning is currently facing.
