The shortcomings of reinforcement learning

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.

 

Resource efficiency

Current deep reinforcement learning algorithms require vast amounts of time, training data, and computational resources in order...