What defines reinforcement learning?

Reinforcement learning is defined by the concept of an agent learning to make decisions by interacting with an environment, where it receives feedback in the form of rewards or penalties based on its actions. This learning process is driven by the agent's goal to maximize cumulative rewards over time. The agent explores various actions and learns which actions yield the most favorable outcomes, thereby improving its policy for decision-making. This core principle of learning from feedback is what distinctly characterizes reinforcement learning, setting it apart from other learning paradigms.

The other options don't encapsulate the essence of reinforcement learning. Learning based on unprocessed data suggests a lack of interaction and feedback from the environment, which is not aligned with how reinforcement learning functions. Learning through human feedback and instruction implies a dependency on supervision, which is more characteristic of supervised learning rather than the autonomous learning approach of reinforcement learning. Finally, learning without any environmental interaction contradicts the fundamental premise of reinforcement learning, where the agent's engagement with its environment is crucial for learning.