User Guide

Introduction

Welcome to rlberry. Use rlberry’s ExperimentManager to train, evaluate and compare rl agents. Like other popular rl libraries, rlberry also provides basic tools for plotting, multiprocessing and logging . In this user guide, we take you through the core features of rlberry and illustrate them with examples and API documentation .

To run all the examples, you will need to install other libraries like “rlberry-scool” (and others).

The easiest way to do it is :

pip install rlberry[torch,extras]
pip install rlberry-scool

rlberry-scool : It’s the repository used for teaching purposes. These are mainly basic agents and environments, in a version that makes it easier for students to learn.

You can find more details about installation here!

You can find our quick starts here :

Set up an experiment

Experimenting with Deep agents

(In construction)

Reproducibility

Advanced Usage

  • Custom Agents (In construction)

  • Custom Environments (In construction)

  • Transfer Learning (In construction)

Contributing to rlberry

If you want to contribute to rlberry, check out the contribution guidelines.