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

Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna

Unit 4. Policy Gradient with PyTorch

Introduction What are the policy-based methods? The advantages and disadvantages of policy-gradient methods Diving deeper into policy-gradient (Optional) the Policy Gradient Theorem Glossary Hands-on Quiz Conclusion Additional Readings

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 unit! There was a lot of information. And congrats on finishing the tutorial. You’ve just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.

Don’t hesitate to iterate on this unit by improving the implementation for more complex environments (for instance, what about changing the network to a Convolutional Neural Network to handle frames as observation)?

In the next unit, we’re going to learn more about Unity MLAgents, by training agents in Unity environments. This way, you will be ready to participate in the AI vs AI challenges where you’ll train your agents to compete against other agents in a snowball fight and a soccer game.

Sound fun? See you next time!

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

←Quiz Additional Readings→