Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. It aims to maximize cumulative rewards over time by learning from the consequences of its actions. The agent receives feedback in the form of rewards or penalties based on its actions and adjusts its behavior accordingly. Over time, through trial and error, the agent learns an optimal policy for decision-making.
RL differs from other learning paradigms in that it is focused on learning from interaction rather than from pre-labeled data. It is often used in scenarios where explicit supervision is not possible, such as in robotics, gaming, and autonomous vehicles. The agent’s goal is to find a strategy that maximizes long-term rewards rather than immediate gratification.
A common example is training a robot to navigate through a maze: the robot receives positive feedback for reaching the goal and negative feedback for making incorrect moves. Through repeated interactions, it refines its behavior to reach the goal efficiently.