RL-- Introduction to Deep Reinforcement Learning – Jonathan Hui – Medium

Deep reinforcement learning is about taking the best actions from what we see and hear. Unfortunately, reinforcement learning RL has a high barrier in learning the concepts and the lingos. In this article, we will cover deep RL with an overview of the general landscape. Yet, we will not shy away from equations and lingos. They provide the basics in understanding the concepts deeper. We will not appeal to you that it only takes 20 lines of code to tackle an RL problem. The official answer should be one! But we will try hard to make it approachable. In most AI topics, we create mathematical frameworks to tackle problems. For RL, the answer is the Markov Decision Process (MDP). It sounds complicated but it produces an easy framework to model a complex problem. An agent (e.g. a human) observes the environment and takes actions. Rewards are given out but they may be infrequent and delayed. Very often, the long-delayed rewards make it extremely hard to untangle the information and traceback what sequence of actions contributed to the rewards.