Deep RL Course documentation

Play with Huggy

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

Introduction How Huggy works? Train Huggy Play with Huggy Conclusion

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

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

Play with Huggy

Now that you’ve trained Huggy and pushed it to the Hub. You will be able to play with him ❤️

For this step it’s simple:

Open the Huggy game in your browser: https://huggingface.co/spaces/ThomasSimonini/Huggy
Click on Play with my Huggy model

load-huggy

In step 1, choose your model repository which is the model id (in my case ThomasSimonini/ppo-Huggy).
In step 2, choose which model you want to replay:

I have multiple ones, since we saved a model every 500000 timesteps.
But if I want the most recent one I choose Huggy.onnx

👉 It’s good to try with different model checkpoints to see the improvement of the agent.

< > Update on GitHub

←Train Huggy Conclusion→