We learned what ML-Agents is and how it works. We also studied the two environments we’re going to use. Now we’re ready to train our agents!
To validate this hands-on for the certification process, you just need to push your trained models to the Hub. There are no minimum results to attain in order to validate this Hands On. But if you want to get nice results, you can try to reach the following:
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
To start the hands-on, click on Open In Colab button 👇 :
We strongly recommend students use Google Colab for the hands-on exercises instead of running them on their personal computers.
By using Google Colab, you can focus on learning and experimenting without worrying about the technical aspects of setting up your environments.
In this notebook, you’ll learn about ML-Agents and train two agents.
After that, you’ll be able to watch your agents playing directly on your browser.
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
⬇️ Here is an example of what you will achieve at the end of this unit. ⬇️
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.
At the end of the notebook, you will:
Before diving into the notebook, you need to:
🔲 📚 Study what ML-Agents is and how it works by reading Unit 5 🤗
Runtime > Change Runtime type
Hardware Accelerator > GPU
# Clone the repository
git clone --depth 1 https://github.com/Unity-Technologies/ml-agents# Go inside the repository and install the package
cd ml-agents
pip install -e ./ml-agents-envs
pip install -e ./ml-agentsIf you need a refresher on how this environment works check this section 👉 https://huggingface.co/deep-rl-course/unit5/snowball-target
./training-envs-executables/linux/# Here, we create training-envs-executables and linux
mkdir ./training-envs-executables
mkdir ./training-envs-executables/linuxDownload the file SnowballTarget.zip from https://drive.google.com/file/d/1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5 using wget.
Check out the full solution to download large files from GDrive here
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=1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1YHHLjyj6gaZ3Gemx1hQgqrPgSS2ZhmB5" -O ./training-envs-executables/linux/SnowballTarget.zip && rm -rf /tmp/cookies.txtWe unzip the executable.zip file
unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/SnowballTarget.zip
Make sure your file is accessible
chmod -R 755 ./training-envs-executables/linux/SnowballTargetThere are multiple hyperparameters. To understand them better, you should read the explanation for each one in the documentation
You need to create a SnowballTarget.yaml config file in ./content/ml-agents/config/ppo/
We’ll give you a preliminary version of this config (to copy and paste into your SnowballTarget.yaml file), but you should modify it.
behaviors:
SnowballTarget:
trainer_type: ppo
summary_freq: 10000
keep_checkpoints: 10
checkpoint_interval: 50000
max_steps: 200000
time_horizon: 64
threaded: true
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
batch_size: 128
buffer_size: 2048
beta: 0.005
epsilon: 0.2
lambd: 0.95
num_epoch: 3
network_settings:
normalize: false
hidden_units: 256
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
As an experiment, try to modify some other hyperparameters. Unity provides very good documentation explaining each of them here.
Now that you’ve created the config file and understand what most hyperparameters do, we’re ready to train our agent 🔥.
To train our agent, we need to launch mlagents-learn and select the executable containing the environment.
We define four parameters:
mlagents-learn <config>: the path where the hyperparameter config file is.--env: where the environment executable is.--run_id: the name you want to give to your training run id.--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 the first time if and when you use
--resume. Try rerunning the block to bypass the error.
The training will take 10 to 35min depending on your config. Go take a ☕️ you deserve it 🤗.
mlagents-learn ./config/ppo/SnowballTarget.yaml --env=./training-envs-executables/linux/SnowballTarget/SnowballTarget --run-id="SnowballTarget1" --no-graphicsTo 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 store your authentication token from the Hugging Face website.
from huggingface_hub import notebook_login
notebook_login()If you don’t want to use Google Colab or a Jupyter Notebook, you need to use this command instead: huggingface-cli login
Then we need to run mlagents-push-to-hf.
And we define four parameters:
--run-id: the name of the training run id.--local-dir: where the agent was saved, it’s results/<run_id name>, so in my case results/First Training.--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--commit-message: since HF repos are git repositories you need to give a commit message.
For instance:
mlagents-push-to-hf --run-id="SnowballTarget1" --local-dir="./results/SnowballTarget1" --repo-id="ThomasSimonini/ppo-SnowballTarget" --commit-message="First Push"
mlagents-push-to-hf --run-id= # Add your run id --local-dir= # Your local dir --repo-id= # Your repo id --commit-message= # Your commit messageIf everything worked you should see 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-SnowballTargetIt’s the link to your model. It contains a model card that explains how to use it, your Tensorboard, 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, etc.
But now comes the best: being able to visualize your agent online 👀.
This step it’s simple:
Remember your repo-id
Go here: https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget
Launch the game and put it in full screen by clicking on the bottom right button
In step 1, choose your model repository, which is the model id (in my case ThomasSimonini/ppo-SnowballTarget).
In step 2, choose what model you want to replay:
SnowballTarget.onnx👉 It’s nice to try different model stages to see the improvement of the agent.
And don’t hesitate to share the best score your agent gets on discord in the #rl-i-made-this channel 🔥
Now let’s try a more challenging environment called Pyramids.
./training-envs-executables/linux/Download the file Pyramids.zip from https://drive.google.com/uc?export=download&id=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H using wget. Check out the full solution to download large files from GDrive here
!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=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1UiFNdKlsH0NTu32xV-giYUEVKV4-vc7H" -O ./training-envs-executables/linux/Pyramids.zip && rm -rf /tmp/cookies.txtUnzip it
%%capture
!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/Pyramids.zipMake sure your file is accessible
chmod -R 755 ./training-envs-executables/linux/Pyramids/PyramidsFor this training, we’ll modify one thing:
As an experiment, you should also try to modify some other hyperparameters. Unity provides very good documentation explaining each of them here.
We’re now ready to train our agent 🔥.
The training will take 30 to 45min depending on your machine, go take a ☕️ you deserve it 🤗.
mlagents-learn ./config/ppo/PyramidsRND.yaml --env=./training-envs-executables/linux/Pyramids/Pyramids --run-id="Pyramids Training" --no-graphicsmlagents-push-to-hf --run-id= # Add your run id --local-dir= # Your local dir --repo-id= # Your repo id --commit-message= # Your commit message👉 https://huggingface.co/spaces/unity/ML-Agents-Pyramids
Now that you know how to train an agent using MLAgents, why not try another environment?
MLAgents provides 17 different environments and we’re building some custom ones. The best way to learn is to try things on your own, have fun.
You have the full list of the one currently available environments on Hugging Face here 👉 https://github.com/huggingface/ml-agents#the-environments
For the demos to visualize your agent 👉 https://huggingface.co/unity
For now we have integrated:
That’s all for today. Congrats on finishing this tutorial!
The best way to learn is to practice and try stuff. Why not try another environment? ML-Agents has 18 different environments, but you can also create your own. Check the documentation and have fun!
See you on Unit 6 🔥,