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

Introduction Optuna Hands-on

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

Hands-on

Now that you’ve learned to use Optuna, here are some ideas to apply what you’ve learned:

1️⃣ Beat your LunarLander-v2 agent results, by using Optuna to find a better set of hyperparameters. You can also try with another environment, such as MountainCar-v0 and CartPole-v1.

2️⃣ Beat your SpaceInvaders agent results.

By doing this, you’ll see how valuable and powerful Optuna can be in training better agents.

Have fun!

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 out this form

Keep Learning, stay awesome 🤗

Update on GitHub

←Optuna Introduction→