What method is described as training a model through interactions to optimize actions toward a specific goal?

The method described, involving training a model through interactions to optimize actions toward a specific goal, is called reinforcement learning. In reinforcement learning, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on its actions. The objective of the agent is to learn a policy that maximizes the cumulative reward over time, effectively optimizing its behavior toward achieving a defined goal.

Reinforcement learning is distinct because it relies on a trial-and-error approach, where the agent explores various actions and learns from the consequences of those actions instead of being provided with explicit labels or training data for specific inputs. This characteristic allows the model to adapt and improve its performance based on the interactions, making it particularly suitable for scenarios where the optimal actions are not known in advance.

In contrast, the other methods focus on different learning paradigms. Supervised learning uses labeled data to train models to predict outcomes based on input features, while unsupervised learning focuses on finding patterns or groupings in data without explicit labels. Transfer learning involves leveraging knowledge gained from one task to improve performance on a related task, but it does not involve the interaction and reward feedback framework central to reinforcement learning.