How does "reinforcement learning" differ from supervised learning?

Reinforcement learning is characterized by its unique approach to training models through trial and error, where an agent learns to make decisions by interacting with an environment. The primary goal is to maximize cumulative rewards based on the outcomes of actions it takes. This process emphasizes exploration and exploitation, enabling the model to adapt its behavior over time to achieve better results based on feedback received from the environment.

In contrast, supervised learning operates on a fundamentally different principle. It relies on a dataset consisting of labeled input-output pairs, where the model learns to predict the output from the input based on examples it processes. The learning process is guided by a clear objective to minimize the error between the predictions and true labels, which does not involve the same trial-and-error mechanism as reinforcement learning.

The distinction lies in how feedback is presented and utilized for training: reinforcement learning adapts based on rewards received after actions taken, while supervised learning learns from explicitly provided correct answers. This fundamental difference in approach and feedback mechanism underlies the distinction highlighted in the correct answer.