Deep RL Course documentation

Introduction

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

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

Introduction Model-Based Reinforcement Learning Offline vs. Online Reinforcement Learning Generalisation Reinforcement Learning Reinforcement Learning from Human Feedback Decision Transformers and Offline RL Language models in RL (Automatic) Curriculum Learning for RL Interesting environments to try An introduction to Unreal Learning Agents An Introduction to Godot RL Students projects Brief introduction to RL documentation

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

Introduction

Unit bonus 3 thumbnail

Congratulations on finishing this course! You now have a solid background in Deep Reinforcement Learning. But this course was just the beginning of your Deep Reinforcement Learning journey, there are so many subsections to discover. In this optional unit, we give you resources to explore multiple concepts and research topics in Reinforcement Learning.

Contrary to other units, this unit is a collective work of multiple people from Hugging Face. We mention the author for each unit.

Sound fun? Let’s get started 🔥,

< > Update on GitHub

←Conclusion Model-Based Reinforcement Learning→