Meaning of Reinforcement Learning

Simple definition

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving rewards or penalties based on its actions.

How to use Reinforcement Learning in a professional context

It’s used in areas like robotics, gaming, autonomous vehicles, and recommendation systems.

Concrete example of Reinforcement Learning

An AI playing chess learns strategies through trial and error, earning rewards for winning games and penalties for losing.

What is the goal of reinforcement learning?

The goal is for the agent to maximize cumulative rewards over time by learning from its actions.

Can reinforcement learning be used in real-world applications?

Yes, it’s applied in robotics, video game AI, and automated decision-making systems.

How is reinforcement learning different from supervised learning?

In supervised learning, the model is trained with labeled data, while in reinforcement learning, the agent learns from the consequences of its actions.