Reinforcement Learning: Teaching AI Through Trial and Error

Public lesson preview

Learn how reinforcement learning works — the training approach behind AlphaGo, ChatGPT's RLHF, game-playing AI, and robotics.

Learning objectives

By the end of this lesson, you will understand:

• ✓🎯 The Core Framework: Agent, Environment, Reward
• ✓🔍 Exploration vs Exploitation: The Central Dilemma
• ✓📊 Markov Decision Processes (MDPs)
• ✓🧮 Q-Learning: Value-Based RL
• ✓🚀 Policy Gradient Methods

Lesson outline

1. 🎯 The Core Framework: Agent, Environment, Reward
2. 🔍 Exploration vs Exploitation: The Central Dilemma
3. 📊 Markov Decision Processes (MDPs)
4. 🧮 Q-Learning: Value-Based RL
5. 🚀 Policy Gradient Methods
6. 💬 RLHF: How ChatGPT Was Tuned
7. ♟️ The AlphaGo Story
8. 🤖 Robotics Applications
9. ⚠️ Limitations of Reinforcement Learning
10. Key Takeaways