  This paper presents a review of the field of reinforcement learning (RL),
with a focus on providing a comprehensive overview of the key concepts,
techniques, and algorithms for beginners. RL has a unique setting, jargon, and
mathematics that can be intimidating for those new to the field or artificial
intelligence more broadly. While many papers review RL in the context of
specific applications, such as games, healthcare, finance, or robotics, these
papers can be difficult for beginners to follow due to the inclusion of
non-RL-related work and the use of algorithms customized to those specific
applications. To address these challenges, this paper provides a clear and
concise overview of the fundamental principles of RL and covers the different
types of RL algorithms. For each algorithm/method, we outline the main
motivation behind its development, its inner workings, and its limitations. The
presentation of the paper is aligned with the historical progress of the field,
from the early 1980s Q-learning algorithm to the current state-of-the-art
algorithms such as TD3, PPO, and offline RL. Overall, this paper aims to serve
as a valuable resource for beginners looking to construct a solid understanding
of the fundamentals of RL and be aware of the historical progress of the field.
It is intended to be a go-to reference for those interested in learning about
RL without being distracted by the details of specific applications.
