Reinforcement

Reinforcement learning:

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards for good actions and penalties for bad actions, and its goal is to maximize the total reward over time. Unlike supervised learning, there are no labeled input-output pairs; the agent learns through trial and error.

Example

Training a robot to navigate a maze. The robot gets a positive reward when it moves closer to the exit and a negative reward if it hits a wall. Over time, it learns the best path to reach the exit efficiently.