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

Unit 3. Deep Q-Learning with Atari Games

Introduction From Q-Learning to Deep Q-Learning The Deep Q-Network (DQN) The Deep Q Algorithm Glossary Hands-on Quiz Conclusion Additional Readings

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

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 tutorial. You’ve just trained your first Deep Q-Learning agent and shared it on the Hub 🥳.

Take time to really grasp the material before continuing.

Don’t hesitate to train your agent in other environments (Pong, Seaquest, QBert, Ms Pac Man). The best way to learn is to try things on your own!

Environments

In the next unit, we’re going to learn about Optuna. One of the most critical tasks in Deep Reinforcement Learning is to find a good set of training hyperparameters. Optuna is a library that helps you to automate the search.

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 🤗

←Quiz Additional Readings→