What is the main principle of reinforcement learning?

Reinforcement learning is primarily concerned with how agents ought to take actions in an environment in order to maximize some notion of cumulative reward. This approach is fundamentally centered on the idea of learning through interaction with that environment, where the agent receives feedback in the form of rewards or punishments based on its actions.

In this context, an agent explores different actions to see how they lead to various outcomes, gradually learning which actions yield the most significant rewards over time. This trial-and-error method allows the agent to develop a strategy that optimizes its decision-making process to achieve specified goals.

While other methods of learning exist, such as learning through direct instruction or using historical data, these do not reflect the essence of reinforcement learning, which is deeply rooted in the dynamics of exploration, reward feedback, and iterative improvement.