What is defined as a type of machine learning where an agent learns through rewards?

Reinforcement learning is defined as a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. This approach mimics how humans and animals learn through trial and error. The key component of reinforcement learning is the reward system, which incentivizes the agent for taking actions that lead to desired outcomes while also discouraging actions that result in negative consequences.

In this learning paradigm, the agent aims to maximize its cumulative reward over time by developing a policy, which is a strategy that defines the best action to take in a given state. Unlike supervised learning, where a model is trained on labeled data, or unsupervised learning, which involves finding patterns without specific guidance, reinforcement learning focuses on learning through interaction and feedback, making it distinct in the realm of machine learning techniques.