The type of learning that occurs when an agent learns from the consequences of actions to maximize rewards is known as reinforcement learning. In this approach, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on its actions. Through trial and error, the agent learns to make decisions that increase the cumulative reward over time. This method is commonly applied in robotics, game playing, and various optimization problems.