Hands-on

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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!

Environments

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 👇 :

Open In Colab

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.

Unit 5: An Introduction to ML-Agents

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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. ⬇️

Pyramids SnowballTarget

🎮 Environments:

📚 RL-Library:

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:

Prerequisites 🏗️

Before diving into the notebook, you need to:

🔲 📚 Study what ML-Agents is and how it works by reading Unit 5 🤗

Let’s train our agents 🚀

Set the GPU 💪

GPU Step 1 GPU Step 2

Clone the repository and install the dependencies 🔽

# 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-agents

SnowballTarget ⛄

If you need a refresher on how this environment works check this section 👉 https://huggingface.co/deep-rl-course/unit5/snowball-target

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

# Here, we create training-envs-executables and linux
mkdir ./training-envs-executables
mkdir ./training-envs-executables/linux

Download 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.txt

We 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/SnowballTarget

Define the SnowballTarget config file

There 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
Config SnowballTarget Config SnowballTarget

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 🔥.

Train the agent

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

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.
MlAgents learn

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-graphics

Push the agent to the Hugging Face Hub

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 store your authentication token from the Hugging Face website.

Create HF Token
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:

  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 repositories you need to give a commit message.
Push to Hub

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 message

If 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-SnowballTarget

It’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 👀.

Watch your agent playing 👀

This step it’s simple:

  1. Remember your repo-id

  2. Go here: https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget

  3. Launch the game and put it in full screen by clicking on the bottom right button

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

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

👉 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.

Pyramids 🏆

Download and move the environment zip file in ./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.txt

Unzip it

%%capture
!unzip -d ./training-envs-executables/linux/ ./training-envs-executables/linux/Pyramids.zip

Make sure your file is accessible

chmod -R 755 ./training-envs-executables/linux/Pyramids/Pyramids

Modify the PyramidsRND config file

For this training, we’ll modify one thing:

Pyramids config

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 🔥.

Train the 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-graphics

Push the agent to the Hugging Face Hub

mlagents-push-to-hf  --run-id= # Add your run id  --local-dir= # Your local dir  --repo-id= # Your repo id  --commit-message= # Your commit message

Watch your agent playing 👀

👉 https://huggingface.co/spaces/unity/ML-Agents-Pyramids

🎁 Bonus: Why not train on another environment?

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.

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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 🔥,

Keep Learning, Stay awesome 🤗