What is defined as "reinforcement learning" in AI?

Reinforcement learning is a specific type of machine learning that focuses on training algorithms to make sequences of decisions by interacting with an environment. In this paradigm, the algorithm learns to achieve a goal by receiving feedback in the form of rewards or penalties based on its actions. The objective is to develop a policy that maximizes cumulative rewards over time, effectively teaching the model which actions lead to the most favorable outcomes.

This approach contrasts with other machine learning methods that typically rely on labeled datasets and direct supervision. In reinforcement learning, the model learns through trial and error, exploring different actions and learning from both the successes and failures. This characteristic makes reinforcement learning particularly useful in dynamic environments where the system must adapt to changing conditions and discover optimal strategies through experience.

Other options may pertain to different aspects of AI but do not capture the essence of reinforcement learning, which is centered on the reward-based learning mechanism.