Define reinforcement learning.

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent takes actions based on the current state of that environment, and in return, it receives feedback in the form of rewards or penalties. This feedback helps the agent understand which actions are beneficial and which are not, allowing it to learn optimal strategies over time.

The core of reinforcement learning is its focus on learning through trial and error, which contrasts with other forms of learning such as supervised learning that relies on labeled datasets. Reinforcement learning emphasizes the importance of exploration and exploitation; the agent must explore different actions to discover their effects while also exploiting known strategies that yield the best rewards. This unique characteristic is what distinguishes it in the broader scope of machine learning techniques.