Reinforcement learning is a feedback-based machine learning approach where an agent interacts with an environment, receives rewards as feedback for its actions. Positive rewards are given for correct actions and negative rewards for incorrect actions. The goal is for the agent to learn to take actions that lead to positive rewards, guiding it towards achieving its objectives. The agent analyzes the environment, performs actions, receives rewards based on the actions taken, and adjusts its strategy accordingly to maximize future rewards. Through this process, the agent learns which actions are beneficial and which are not, gradually improving its decision-making abilities over time.