README.md

Q-learning to drive a taxi 🚕

You talkin' to me?

-- Robert de Niro (Taxi driver)

Venice’s taxis 👆 by Helena Jankovičová Kováčová from Pexels 🙏

Table of Contents

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

Welcome 🤗

This is part 1 of the Hands-on RL course.

Let's use (tabular) Q-learning to teach an agent to solve the Taxi-v3 environment from OpenAI gym.

Fasten your seat belt and get ready. We are ready to depart!

Quick setup

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

1. Pull the code from GitHub and cd into the 01_taxi folder:

   $ git clone https://github.com/Paulescu/hands-on-rl.git
   $ cd hands-on-rl/01_taxi
   

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 😉.

Lecture transcripts

📝 Q learning

Notebooks

• Explore the environment
• Random agent baseline
• Q-agent
• Hyperparameter tuning
• Homework

Let's connect!

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