Deep RL Course documentation

Let&#39;s train and play with Huggy 🐶

Join the Hugging Face community

to get started

Let's train and play with Huggy 🐶

Let's train Huggy 🐶

To start to train Huggy, click on Open In Colab button 👇 :

In this notebook, we’ll reinforce what we learned in the first Unit by teaching Huggy the Dog to fetch the stick and then play with it directly in your browser

⬇️ Here is an example of what you will achieve at the end of the unit. ⬇️ (launch ▶ to see)

%%html
<video controls autoplay><source src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit-bonus1/huggy.mp4" type="video/mp4"></video>

The library used 📚

We’re constantly trying to improve our tutorials, so if you find some issues in this notebook, please open an issue on the Github Repo.

Objectives of this notebook 🏆

At the end of the notebook, you will:

• Understand the state space, action space and reward function used to train Huggy.
• Train your own Huggy to fetch the stick.
• Be able to play with your trained Huggy directly in your browser.

Prerequisites 🏗️

Before diving into the notebook, you need to:

🔲 📚 Develop an understanding of the foundations of Reinforcement learning (MC, TD, Rewards hypothesis…) by doing Unit 1

🔲 📚 Read the introduction to Huggy by doing Bonus Unit 1

Set the GPU 💪

- To **accelerate the agent's training, we'll use a GPU**. To do that, go to Runtime > Change Runtime type
• Hardware Accelerator > GPU

Clone the repository and install the dependencies 🔽

• We need to clone the repository, that contains the experimental version of the library that allows you to push your trained agent to the Hub.
# Clone this specific repository (can take 3min)
git clone --depth 1 --branch hf-integration https://github.com/huggingface/ml-agents
# Go inside the repository and install the package (can take 3min)
%cd ml-agents
pip3 install -e ./ml-agents-envs
pip3 install -e ./ml-agents

Download and move the environment zip file in ./trained-envs-executables/linux/

• Our environment executable is in a zip file.
• We need to download it and place it to ./trained-envs-executables/linux/
mkdir ./trained-envs-executables
mkdir ./trained-envs-executables/linux
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=\$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1zv3M95ZJTWHUVOWT6ckq_cm98nft8gdF' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1zv3M95ZJTWHUVOWT6ckq_cm98nft8gdF" -O ./trained-envs-executables/linux/Huggy.zip && rm -rf /tmp/cookies.txt

Download the file Huggy.zip from https://drive.google.com/uc?export=download&id=1zv3M95ZJTWHUVOWT6ckq_cm98nft8gdF using wget. Check out the full solution to download large files from GDrive here

%%capture
unzip -d ./trained-envs-executables/linux/ ./trained-envs-executables/linux/Huggy.zip

Make sure your file is accessible

chmod -R 755 ./trained-envs-executables/linux/Huggy

Let's recap how this environment works

The State Space: what Huggy perceives.

Huggy doesn’t “see” his environment. Instead, we provide him information about the environment:

• The target (stick) position
• The relative position between himself and the target
• The orientation of his legs.

Given all this information, Huggy can decide which action to take next to fulfill his goal.

The Action Space: what moves Huggy can do

Joint motors drive huggy legs. It means that to get the target, Huggy needs to learn to rotate the joint motors of each of his legs correctly so he can move.

The Reward Function

The reward function is designed so that Huggy will fulfill his goal : fetch the stick.

Remember that one of the foundations of Reinforcement Learning is the reward hypothesis: a goal can be described as the maximization of the expected cumulative reward.

Here, our goal is that Huggy goes towards the stick but without spinning too much. Hence, our reward function must translate this goal.

Our reward function:

• Orientation bonus: we reward him for getting close to the target.
• Time penalty: a fixed-time penalty given at every action to force him to get to the stick as fast as possible.
• Rotation penalty: we penalize Huggy if he spins too much and turns too quickly.
• Getting to the target reward: we reward Huggy for reaching the target.

Check the Huggy config file

• In ML-Agents, you define the training hyperparameters into config.yaml files.

• For the scope of this notebook, we’re not going to modify the hyperparameters, but if you want to try as an experiment, you should also try to modify some other hyperparameters, Unity provides very good documentation explaining each of them here.

• In the case you want to modify the hyperparameters, in Google Colab notebook, you can click here to open the config.yaml: /content/ml-agents/config/ppo/Huggy.yaml

We’re now ready to train our agent 🔥.

Train our agent

To train our agent, we just need to launch mlagents-learn and select the executable containing the environment.

With ML Agents, we run a training script. We define four parameters:

1. mlagents-learn <config>: the path where the hyperparameter config file is.
2. --env: where the environment executable is.
3. --run_id: the name you want to give to your training run id.
4. --no-graphics: to not launch the visualization during the training.

Train the model and use the --resume flag to continue training in case of interruption.

It will fail first time when you use --resume, try running the block again to bypass the error.

The training will take 30 to 45min depending on your machine (don’t forget to set up a GPU), go take a ☕️you deserve it 🤗.

mlagents-learn ./config/ppo/Huggy.yaml --env=./trained-envs-executables/linux/Huggy/Huggy --run-id="Huggy" --no-graphics

Push the agent to the 🤗 Hub

• Now that we trained our agent, we’re ready to push it to the Hub to be able to play with Huggy on your browser🔥.

To be able to share your model with the community there are three more steps to follow:

1️⃣ (If it’s not already done) create an account to HF ➡ https://huggingface.co/join

2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.

• Copy the token
• Run the cell below and paste the token
from huggingface_hub import notebook_login

notebook_login()

If you don’t want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: huggingface-cli login

Then, we simply need to run mlagents-push-to-hf.

And we define 4 parameters:

1. --run-id: the name of the training run id.
2. --local-dir: where the agent was saved, it’s results/<run_id name>, so in my case results/First Training.
3. --repo-id: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name> If the repo does not exist it will be created automatically
4. --commit-message: since HF repos are git repository you need to define a commit message.
mlagents-push-to-hf --run-id="HuggyTraining" --local-dir="./results/Huggy" --repo-id="ThomasSimonini/ppo-Huggy" --commit-message="Huggy"

Else, if everything worked you should have this at the end of the process(but with a different url 😆) :

Your model is pushed to the hub. You can view your model here: https://huggingface.co/ThomasSimonini/ppo-Huggy

It’s the link to your model repository. The repository contains a model card that explains how to use the model, your Tensorboard logs and your config file. What’s awesome is that it’s a git repository, which means you can have different commits, update your repository with a new push, open Pull Requests, etc.

But now comes the best: being able to play with Huggy online 👀.

This step is the simplest:

1. In step 1, choose your model repository which is the model id (in my case ThomasSimonini/ppo-Huggy).

2. In step 2, choose what model you want to replay:

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

👉 What’s nice is to try with different models steps to see the improvement of the agent.

Congrats on finishing this bonus unit!

You can now sit and enjoy playing with your Huggy 🐶. And don’t forget to spread the love by sharing Huggy with your friends 🤗. And if you share about it on social media, please tag us @huggingface and me @simoninithomas