Renforcement Learning

ELI5 (Explain Like I'm 5)

Think of training a pup. When it does the right thing (sit), it gets a treat. When it does the wrong thing (jumping onto the dining table), it doesn’t. Over time, the pup figures out which actions lead to treats.

Reinforcement learning is similar. The AI tries actions, sees what reward it gets (positive or negative score), and slowly learns to earn more rewards.

How Reinforcement Learning Works

The goal of reinforcement learning is not to predict a correct answer, but to learn a strategy that maximizes rewards over time.

RL has 4 key components.

1. Agent – the learner or decision-maker
2. Environment – the system the agent interacts with
3. Action – a choice the agent makes
4. Reward – feedback on how good that action was

Learning happens through repeated trial and error.

The agent gradually learns which actions lead to better long-term rewards, even if short-term rewards are sometimes negative.

Why Reinforcement Learning Matters in AI

Reinforcement learning is especially useful when:

• Decisions are made sequentially
• The best action depends on long-term consequences
• Explicit instructions are unavailable

The best real life example is a robot vacuum cleaner. It learns the most efficient way to clean a house by trying routes, bumping into obstacles, and improving over time based on how much area it covers.

RL is very challenging. Training can be very slow, reward design is tricky, and mistakes committed during learning can be costly in the real-world. Therefore reinforcement learning is often used in controlled or simulated environments before being deployed.