Reinforcement Learning

Reinforcement learning (RL) is a subset of machine learning that allows an artificial system (sometimes referred to as an agent) in a given environment to optimize its behaviour. Agents learn from feedback signals received as a result of their actions, such as rewards or punishments, with the aim of maximizing the received reward. Such signals are computed based on a given reward function, which constitutes an abstract representation of the system's goal. The goal could be, for example, to earn a high video game score or to minimize idle worker time in a factory