Which type of learning involves an agent improving its actions based on a system of rewards and penalties?

Transfer learning involves reusing a pre-trained model on a new, related task, not learning through rewards and penalties.

Unsupervised learning deals with finding patterns or structures in unlabeled data without explicit rewards or penalties.

Reinforcement learning specifically involves an agent interacting with an environment, performing actions, and receiving feedback in the form of rewards or penalties, which it uses to learn a policy for maximizing cumulative reward. This iterative process of exploration and exploitation drives the agent to improve its decision-making over time.

Supervised learning involves training a model on labeled datasets to predict outputs based on input features, where the correct answers are provided for learning.