Which of the following represents a reward-based learning process?

The choice of reinforcement learning is correct because it is explicitly designed as a reward-based learning process. In reinforcement learning, an agent interacts with an environment and learns to make decisions by receiving feedback in the form of rewards or penalties based on its actions. The goal is to maximize cumulative rewards over time, which shapes the learning process. The agent explores different actions, assesses the rewards they produce, and adjusts its strategy accordingly, thus learning optimal behavior through trial and error.

In contrast, supervised learning involves training a model on a labeled dataset, providing it with input-output pairs to learn from, but it does not directly involve the concept of rewards in the same way as reinforcement learning. Deep learning is an advanced subset of machine learning techniques that can be applied within supervised learning contexts but also does not inherently relate to reward-based processes. Narrow AI refers to artificial intelligence systems that are designed for a specific task, and while it may utilize various learning methods, it does not specifically denote a reward-based learning approach.