README.md

Hands-On Reinforcement Learning

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. It must contain a README.md file that explains briefly the successive steps of the project. 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 Monday, February 13, 2023. Subsequent commits will not be considered.

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

pip install gym==0.21

Usage

Here is an example of how to use Gym to solve the Cartpole-v1 environment: