Deep RL Course documentation

Conclusion

Deep RL Course

Unit 0. Welcome to the course

Unit 1. Introduction to Deep Reinforcement Learning

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

Introduction What is RL? A short recap The two types of value-based methods The Bellman Equation, simplify our value estimation Monte Carlo vs Temporal Difference Learning Mid-way Recap Mid-way Quiz Introducing Q-Learning A Q-Learning example Q-Learning Recap Glossary Hands-on Q-Learning Quiz Conclusion Additional Readings

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

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

Conclusion

Congrats on finishing this chapter! There was a lot of information. And congrats on finishing the tutorials. You’ve just implemented your first RL agent from scratch and shared it on the Hub 🥳.

Implementing from scratch when you study a new architecture is important to understand how it works.

It’s normal if you still feel confused by all these elements. This was the same for me and for everyone who studies RL.

Take time to really grasp the material before continuing.

In the next chapter, we’re going to dive deeper by studying our first Deep Reinforcement Learning algorithm based on Q-Learning: Deep Q-Learning. And you’ll train a DQN agent with RL-Baselines3 Zoo to play Atari Games.

Atari environments

Finally, we would love to hear what you think of the course and how we can improve it. If you have some feedback then please 👉 fill this form

Keep Learning, stay awesome 🤗

< > Update on GitHub

←Q-Learning Quiz Additional Readings→