Which machine learning approach is focused on maximizing cumulative rewards?

Reinforcement Learning is a machine learning approach specifically designed to maximize cumulative rewards through interactions with an environment. In this framework, an agent learns to make decisions by taking actions in various states and receiving feedback in the form of rewards or penalties. The goal is to develop a policy that guides the agent in selecting actions that maximize the total reward over time. This is distinctive from other machine learning paradigms.

In contrast, supervised learning aims to learn a function that maps inputs to outputs based on labeled training data, without explicitly considering the concept of rewards. Unsupervised learning is concerned with finding patterns or structures in data without labeled responses, focusing on clustering or dimensionality reduction rather than reward maximization. Deep learning, while it can be integrated into reinforcement learning strategies, refers to a family of algorithms that use neural networks to learn representations of data rather than directly addressing how to maximize rewards.

Thus, reinforcement learning is uniquely characterized by its focus on improving cumulative rewards based on the agent's experience, setting it apart from the other approaches in the list.