Skip to content
Snippets Groups Projects
Select Git revision
  • main default protected
1 result

README.md

Blame
    • README.md 9.61 KiB

    TD 1 : Hands-On Reinforcement Learning

    MSO 3.4 Apprentissage Automatique

    In this hands-on project, we will first implement a simple RL algorithm and apply it to solve the CartPole-v1 environment. Once we become familiar with the basic workflow, we will learn to use various tools for machine learning model training, monitoring, and sharing, by applying these tools to train a robotic arm.

    To be handed in

    This work must be done individually. The expected output is a repository named hands-on-rl on https://gitlab.ec-lyon.fr.

    We assume that git is installed, and that you are familiar with the basic git commands. (Optionnaly, you can use GitHub Desktop.) We also assume that you have access to the ECL GitLab. If necessary, please consult this tutorial.

    Your repository must contain a README.md file that explains briefly the successive steps of the project. It must be private, so you need to add your teacher as "developer" member.

    Throughout the subject, you will find a 🛠️ symbol indicating that a specific production is expected.

    The last commit is due before 11:59 pm on March 17, 2025. Subsequent commits will not be considered.

    ⚠️ Warning Ensure that you only commit the files that are requested. For example, your directory should not contain the generated .zip files, nor the runs folder... At the end, your repository must contain one README.md, three python scripts, and optionally image files for the plots.

    Before you start

    Make sure you know the basics of Reinforcement Learning. In case of need, you can refer to the introduction of the Hugging Face RL course.

    Introduction to Gym

    Gym is a framework for developing and evaluating reinforcement learning environments. It offers various environments, including classic control and toy text scenarios, to test RL algorithms.

    Installation

    We recommend to use Python virtual environnements to install the required modules : https://docs.python.org/3/library/venv.html or https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html if you are using conda.

    First, install Pytorch : https://pytorch.org/get-started/locally.

    Then install the following modules :

    pip install gymnasium