Which of the following is not a characteristic of reinforcement learning?

Reinforcement learning is a type of machine learning that focuses on how agents ought to take actions in an environment to maximize cumulative rewards. One of its core characteristics is that it learns through trial and error, where agents explore various actions and the resultant consequences of those actions, adapting their strategies based on the feedback received.

The mechanism of reinforcement learning is fundamentally based on a reward system that reinforces desired behaviors. This reward system is crucial in guiding the learning process by providing incentives for the agent to choose certain actions over others. The objective is to maximize these cumulative rewards over time, which is a defining trait of reinforcement learning.

In contrast, reinforcement learning does not rely on labeled datasets, which is a characteristic more associated with supervised learning. Labeled datasets consist of input-output pairs where the correct output (label) is provided for each input. In reinforcement learning, the agent interacts with the environment without having access to predefined labels; rather, it learns from the consequences of its actions. This distinction is key to understanding why the absence of labeled datasets is not consistent with the principles of reinforcement learning.