README.md

Policy Gradients to land on the Moon

“That's one small step for your gradient ascent, one giant leap for your ML career.”

-- Pau quoting Neil Armstrong

Table of Contents

• Welcome 🤗
• Lecture transcripts
• Quick setup
• Notebooks
• Let's connect

Welcome 🤗

Today we will learn about Policy Gradient methods, and use them to land on the Moon.

Ready, set, go!

Lecture transcripts

📝 1. Policy gradients

Quick setup

Make sure you have Python >= 3.7. Otherwise, update it.

1. Pull the code from GitHub and cd into the 04_lunar_lander folder:

   $ git clone https://github.com/Paulescu/hands-on-rl.git
   $ cd hands-on-rl/04_lunar_lander
   

2. Make sure you have the virtualenv tool in your Python installation

   $ pip3 install virtualenv
   

3. Create a virtual environment and activate it.

   $ virtualenv -p python3 venv
   $ source venv/bin/activate
   

   From this point onwards commands run inside the virtual environment.

4. Install dependencies and code from src folder in editable mode, so you can experiment with the code.

   $ (venv) pip install -r requirements.txt
   $ (venv) export PYTHONPATH="."
   

5. Open the notebooks, either with good old Jupyter or Jupyter lab

   $ (venv) jupyter notebook
   

   $ (venv) jupyter lab
   

   If both launch commands fail, try these:

   $ (venv) jupyter notebook --NotebookApp.use_redirect_file=False
   

   $ (venv) jupyter lab --NotebookApp.use_redirect_file=False
   

6. Play and learn. And do the homework 😉.

Notebooks

• Random agent baseline
• Policy gradients with rewards as weights
• Policy gradients with rewards-to-go as weights
• Homework

Let's connect!

Do you wanna become a PRO in Machine Learning?

👉🏽 Subscribe to the datamachines newsletter 🧠

👉🏽 Follow me on Twitter and LinkedIn 💡