Reinforcement Learning - How AI Learns by Doing

Public lesson preview

Understand how AI agents learn through trial, error, and reward - from game-playing champions to real-world robots.

Programme: AI Branches
Level: Intermediate
Reading time: 15 min read

Learning objectives

By the end of this lesson, you will understand:

The Agent-Environment Loop
Exploration vs Exploitation
Q-Learning - A Simple but Powerful Idea
Policy Gradient Methods
AlphaGo - The Game That Changed Everything

Lesson outline

The Agent-Environment Loop
Exploration vs Exploitation
Q-Learning - A Simple but Powerful Idea
Policy Gradient Methods
AlphaGo - The Game That Changed Everything
OpenAI Five and Game AI
Real-World Reinforcement Learning
Reward Hacking - When AI Games the System
Sim-to-Real Transfer
📚 Further Reading

Introductory preview

Imagine teaching a puppy to sit. You do not hand it a textbook - you let it try things, and when it sits, you give it a treat. Over time, it learns which actions earn rewards. Reinforcement Learning (RL) works the same way: an AI agent learns by interacting with an environment, receiving feedback, and adjusting its behaviour to maximise long-term reward.

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Reinforcement Learning - How AI Learns by Doing

Learning objectives

Lesson outline

Introductory preview

Continue Reinforcement Learning - How AI Learns by Doing with Pro