Deep RL Course documentation

Introduction to PPO with Sample-Factory

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

Introduction PPO with Sample Factory and Doom Conclusion

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

Introduction to PPO with Sample-Factory

In this second part of Unit 8, we’ll get deeper into PPO optimization by using Sample-Factory, an asynchronous implementation of the PPO algorithm, to train our agent to play vizdoom (an open source version of Doom).

In the notebook, you’ll train your agent to play the Health Gathering level, where the agent must collect health packs to avoid dying. After that, you can train your agent to play more complex levels, such as Deathmatch.

Environment

Sound exciting? Let’s get started! 🚀

The hands-on is made by Edward Beeching, a Machine Learning Research Scientist at Hugging Face. He worked on Godot Reinforcement Learning Agents, an open-source interface for developing environments and agents in the Godot Game Engine.

←Additional Readings PPO with Sample Factory and Doom→