Which type of learning focuses on how agents take actions in an environment to maximize rewards?

Reinforcement learning is a type of machine learning that is centered around agents taking actions in an environment with the goal of maximizing cumulative rewards over time. In this framework, an agent learns from its interactions with the environment by receiving feedback in the form of rewards or penalties based on its actions. This feedback loop allows the agent to adjust its behavior in a way that enhances its future performance.

The core premise of reinforcement learning is the exploration-exploitation trade-off, where the agent must decide whether to explore new actions to gain more knowledge about the environment or exploit known actions that yield higher rewards. This iterative learning process enables the agent to discover the most effective strategies or policies for decision-making in complex environments.

In contrast, other types of learning, such as supervised learning, involves training a model on labeled data to predict outputs based on known inputs, while unsupervised learning aims to find hidden patterns or structures in unlabeled data. Transfer learning focuses on leveraging knowledge gained in one domain to improve learning in another related domain. None of these approaches emphasize the interaction with an environment and the resultant feedback typically found in reinforcement learning.