RL vs Other Learning Paradigms

Machine learning consists of three main learning paradigms, each suited for different types of problems. Reinforcement learning is one of them, alongside supervised learning and unsupervised learning.

RL Key Features

• No labeled data: RL does not require predefined input-output pairs but instead learns from experience;
• Trial and error learning: the agent explores different actions and refines its strategy based on feedback;
• Sequential decision-making: RL is designed for tasks where current decisions affect future outcomes;
• Reward maximization: the learning objective is to optimize long-term rewards rather than short-term correctness.

How Three ML Paradigms Compare

Why is Reinforcement Learning Different

Reinforcement learning shares some similarities with other paradigms, but stands out due to its unique approach to the learning process.

Supervised Learning

In supervised learning, a dataset provides explicit instructions on what the correct output should be.
In reinforcement learning, there is no explicit supervision—the agent must figure out the best actions through experience.

Unsupervised Learning

Unsupervised learning finds hidden patterns in data without specific goals.
Reinforcement learning learns through interaction with an environment to achieve an explicit goal (e.g., winning a game).