Deep Reinforcement Learning

Machine learning is usually classified into different paradigms, such as supervised learning, unsupervised learning, semi-supervised, self-supervised learning, and reinforcement learning (RL). Supervised learning requires labeled data and is currently the most popularly used machine learning paradigm. However, applications based on unsupervised and semi-supervised learning, which require few or no labels, have been steadily on the rise, especially in the form of generative models. Better still, the rise of Large Language Models (LLMs) have shown that self-supervised learning (where labels are implicit within the data) is an even more promising machine learning paradigm.

RL, on the other hand, is a different branch of machine learning that is considered to be the closest we have reached in terms of emulating how humans learn. It is an area of active research and development and is in its early stages, with some promising results. A prominent example...