The correct answer is D. Reinforced learning.
Reinforced learning is a type of machine learning in which an agent learns to take actions in an environment in order to maximize a reward. The agent does this by trial and error, and by observing the consequences of its actions. Over time, the agent learns to take actions that lead to more rewards.
Unsupervised learning is a type of machine learning in which an algorithm learns to find patterns in data without being explicitly told what to look for. This type of learning is often used for tasks such as clustering and dimensionality reduction.
Supervised learning is a type of machine learning in which an algorithm learns to map inputs to outputs by being given a set of labeled training data. This type of learning is often used for tasks such as classification and regression.
Semisupervised learning is a type of machine learning that combines supervised and unsupervised learning. In semisupervised learning, the algorithm is given a set of labeled training data and a set of unlabeled data. The algorithm then uses the labeled data to learn a model, and then uses the unlabeled data to improve the model.
In conclusion, reinforced learning is the type of learning that requires self-assessment to identify patterns within data.