Which method is characteristic of Reinforcement Learning?

Reinforcement Learning is defined by its unique approach to learning, which primarily revolves around the concept of learning from rewards through action feedback. In this framework, an agent interacts with an environment, making decisions (or taking actions) based on its current state. After taking an action, the agent receives feedback in the form of rewards or penalties, which helps it to learn which actions yield the best results over time.

This cycle of exploration (trying new actions) and exploitation (choosing actions that yield high rewards) allows the agent to improve its decision-making capabilities. Unlike other learning paradigms, such as supervised learning or unsupervised learning, Reinforcement Learning does not rely on predefined sets of labeled data or human interventions exclusively. Instead, it is focused on maximizing cumulative rewards through experience gained from interacting with the environment dynamically.

The reinforcement learning approach is widely used in various applications, including robotics, game playing, and automated control systems, where the ability to learn from the consequences of actions rather than from explicit instructions is essential.