Reinforcement Learning(RL) is one of the hottest research topics in the field of modern Artificial Intelligence and its popularity is only growing. Let's look at 5 useful things one needs to know to get started with RL. Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where the feedback provided to the agent is correct set of actions for performing a task, reinforcement learning uses rewards and punishments as signals for positive and negative behavior. As compared to unsupervised learning, reinforcement learning is different in terms of goals.

This article assumes no prior knowledge in Reinforcement Learning, but it does assume some basic understanding of neural networks. Out of all the different types of Machine Learning fields, the one fascinating me the most is Reinforcement Learning. For those who are less familiar with it -- while Supervised Learning deals with predicting values or classes based on labeled data and Unsupervised Learning deals with clustering and finding relations in unlabeled data, Reinforcement Learning deals with how some arbitrary being (formally referred to as an "Agent") should act and behave in a given environment. The way it is done is by giving the Agent rewards or punishments based on the actions it has performed on different scenarios. One of the first practical Reinforcement Learning methods I learned was Deep Q Networks, and I believe it's an excellent kickstart to this journey.