Deep Reinforcement Learning: Zero to Hero!

Welcome to the Adventure!

Deep Reinforcement Learning can feel less accessible than other areas of AI, but it's also one of the most intriguing. It tackles a fundamental question: how can an agent learn to make optimal decisions by interacting with an environment? Instead of being fed answers, the agent learns through trial and error, guided only by a sparse reward signal—much like how humans learn. This course will empower you to build these intelligent agents, from playing Atari games to landing on the Moon and fine-tuning modern Language Models.

Quick Video Introduction

Watch this short video to get a feel for the course and what you'll build.

Course Content

You'll progress through a series of hands-on notebooks, implementing each algorithm from scratch.

0. Introduction: An overview of the course and RL's core concepts.

1. Markov Decision Processes: The mathematical framework for decision-making.

2. RL Foundations: Exploring agents, environments, states, actions, and rewards.

3. Deep Q Learning: The value-based classic that mastered Atari games.

4. Policy Gradient: Methods that directly optimize an agent's policy.

5. Actor Critic Methods: A hybrid approach for more stable learning.

6. Proximal Policy Optimization: An advanced and popular policy gradient algorithm.

7. Bridge to Advanced Topics: Transitioning to more specialized areas of RL.

8. Exploration and Curiosity: Designing agents that explore effectively.

9. Multi-Agent RL: Scenarios where multiple agents interact and learn.

10. Imitation Learning: Training agents by mimicking expert behavior.

11. MCTS & AlphaZero: The powerful search algorithms behind game-playing champions.

12. Productionizing RL: Best practices for deploying RL systems.

13. Model Based RL: Agents that learn a model of the world to plan ahead.

14. RLHF: The technique used to align and fine-tune modern LLMs.

15. Conclusion: A final summary and a look at the future of RL.

Prerequisites

To make the most out of this course, you should be familiar with the following:

🐍 Proficiency in Python

Comfort with classes, data structures, and general control flow.

🧠 Deep Learning & PyTorch

Understanding of neural networks, gradient descent, and PyTorch basics.

🧮 Mathematical Foundations

A solid understanding of basic calculus, linear algebra, and probability is recommended for theory.

About the Course

"I collected lots of separate pieces of code I wrote during the years, and attempted to assemble them in a coherent and unified educational resource." - Alessio