Reinforcement Learning

Reinforcement Learning (RL) is an area of machine learning concerned with how intelligent agents should take actions in an environment to maximize a notion of cumulative reward. Unlike supervised learning, which relies on labeled datasets, or unsupervised learning, which finds patterns in unlabeled data, RL agents learn through trial and error by interacting with an environment. The agent performs an action, the environment responds with a new state and a scalar reward signal, and the agent uses this feedback to refine its strategy or "policy" over time.