What is the goal of reinforcement learning?

The goal of reinforcement learning is to teach an agent how to take actions based on rewards. This approach involves an agent that interacts with an environment and learns to make decisions by receiving feedback in the form of rewards or penalties. The agent aims to maximize the cumulative reward over time by discovering which actions yield the most favorable outcomes in different situations.

In reinforcement learning, the agent explores its environment and evaluates the consequences of its actions, gradually refining its strategies through trial and error. This learning process is fundamentally about optimizing behavior based on feedback, distinguishing it from other learning paradigms that involve direct supervision or unsupervised clustering.

This goal contrasts with the other choices, which describe different aspects of machine learning. Classifying data into distinct categories is more aligned with supervised learning techniques. Analyzing large datasets for underlying trends pertains to data mining or exploratory data analysis. Lastly, promoting collaborative learning among machines describes a more collective approach, often seen in federated learning or multi-agent systems, rather than the individual learning cycle characteristic of reinforcement learning.