Deep RL Course

Unit 0. Welcome to the course

Unit 1. Introduction to Deep Reinforcement Learning

Introduction What is Reinforcement Learning?The Reinforcement Learning Framework The type of tasks The Exploration/ Exploitation tradeoff The two main approaches for solving RL problems The “Deep” in Deep Reinforcement Learning Summary Glossary Hands-on Quiz Conclusion Additional Readings

Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy

Live 1. How the course work, Q&A, and playing with Huggy

Unit 2. Introduction to Q-Learning

Unit 3. Deep Q-Learning with Atari Games

Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna

Unit 4. Policy Gradient with PyTorch

Unit 5. Introduction to Unity ML-Agents

Unit 6. Actor Critic methods with Robotics environments

Unit 7. Introduction to Multi-Agents and AI vs AI

Unit 8. Part 1 Proximal Policy Optimization (PPO)

Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom

Bonus Unit 3. Advanced Topics in Reinforcement Learning

Bonus Unit 5. Imitation Learning with Godot RL Agents

Certification and congratulations

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The “Deep” in Reinforcement Learning

What we've talked about so far is Reinforcement Learning. But where does the "Deep" come into play?

Deep Reinforcement Learning introduces deep neural networks to solve Reinforcement Learning problems — hence the name “deep”.

For instance, in the next unit, we’ll learn about two value-based algorithms: Q-Learning (classic Reinforcement Learning) and then Deep Q-Learning.

You’ll see the difference is that, in the first approach, we use a traditional algorithm to create a Q table that helps us find what action to take for each state.

In the second approach, we will use a Neural Network (to approximate the Q value).

Value based RL — Schema inspired by the Q learning notebook by Udacity

If you are not familiar with Deep Learning you should definitely watch the FastAI Practical Deep Learning for Coders (Free).

Update on GitHub

←The two main approaches for solving RL problems Summary→