Overview#Reinforcement learning is learning that is Supervised Learning and typically associated with Machine Learning
In Reinforcement learning the goal is to develop a system (agent) that improves its performance based on interactions with the environment. Since the information about the current state of the environment typically also includes a so-called reward signal, we can think of Reinforcement learning as a field related to Supervised Learning.
However, in Reinforcement learning this feedback is not the correct ground truth label or value, but a measure of how well the action was measured by a reward function. Through its interaction with the environment, an agent can then use Reinforcement learning to learn a series of actions that maximizes this reward via an exploratory trial-and-error approach or deliberative planning.