Which type of machine learning involves an agent learning to make decisions through interaction with its environment?

Reinforcement learning is the type of machine learning where an agent learns to make decisions through interaction with its environment. In this approach, the agent takes actions in a given state and receives feedback from those actions in the form of rewards or penalties. The goal of the agent is to maximize the cumulative reward over time by learning a policy that dictates the best action to take in each state.

The essence of reinforcement learning lies in its iterative process of trial and error, where the agent explores different strategies and gradually learns which ones yield the best outcomes. This is distinct from the other types of learning listed.

In contrast, supervised learning involves training a model on a labeled dataset, where the correct output for each input is known, and the goal is to learn to predict these outputs. Unsupervised learning focuses on discovering hidden patterns or intrinsic structures in data without labeled responses. Transductive learning is a subset of semi-supervised learning where the model is trained on a small amount of labeled data and makes predictions specifically for a known set of unlabeled instances.

Therefore, reinforcement learning stands out as the method that specifically emphasizes the interaction of an agent with its environment to learn and adapt its decision-making behavior.