"Make It Work Make It Right Make It Fast"


ReinforcementLearning.jl, as the name says, is a package for reinforcement learning research in Julia.

Our design principles are:

  • Reusability and extensibility: Provide elaborately designed components and interfaces to help users implement new algorithms.
  • Easy experimentation: Make it easy for new users to run benchmark experiments, compare different algorithms, evaluate and diagnose agents.
  • Reproducibility: Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning algorithms.

๐Ÿน Get Started

julia> ] add ReinforcementLearning

julia> using ReinforcementLearning

julia> run(
           RandomPolicy(),
           CartPoleEnv(),
           StopAfterStep(1_000),
           TotalRewardPerEpisode()
       )

The above simple example demonstrates four core components in a general reinforcement learning experiment:

Check out the tutorial page to learn how these four components are assembled together to solve many interesting problems. We also write blog occasionally to explain the implementation details of some algorithms. Among them, the most recommended one is An Introduction to ReinforcementLearning.jl, which explains the design idea of this package. Besides, a collection of experiments are also provided to help you understand how to train or evaluate policies, tune parameters, log intermediate data, load or save parameters, plot results and record videos. For example:

๐ŸŒฒ Project Structure

ReinforcementLearning.jl itself is just a wrapper around several other subpackages. The relationship between them is depicted below:

+-----------------------------------------------------------------------------------+
|                                                                                   |
|  ReinforcementLearning.jl                                                         |
|                                                                                   |
|      +------------------------------+                                             |
|      | ReinforcementLearningBase.jl |                                             |
|      +----|-------------------------+                                             |
|           |                                                                       |
|           |     +--------------------------------------+                          |
|           +---->+ ReinforcementLearningEnvironments.jl |                          |
|           |     +--------------------------------------+                          |
|           |                                                                       |
|           |     +------------------------------+                                  |
|           +---->+ ReinforcementLearningCore.jl |                                  |
|                 +----|-------------------------+                                  |
|                      |                                                            |
|                      |     +-----------------------------+                        |
|                      +---->+ ReinforcementLearningZoo.jl |                        |
|                            +----|------------------------+                        |
|                                 |                                                 |
|                                 |     +-------------------------------------+     |
|                                 +---->+ DistributedReinforcementLearning.jl |     |
|                                       +-------------------------------------+     |
|                                                                                   |
+------|----------------------------------------------------------------------------+
       |
       |     +-------------------------------------+
       +---->+ ReinforcementLearningExperiments.jl |
       |     +-------------------------------------+
       |
       |     +----------------------------------------+
       +---->+ ReinforcementLearningAnIntroduction.jl |
             +----------------------------------------+

๐Ÿ–– Supporting

ReinforcementLearning.jl is a MIT licensed open source project with its ongoing development made possible by many contributors in their spare time. However, modern reinforcement learning research requires huge computing resource, which is unaffordable for individual contributors. So if you or your organization could provide the computing resource in some degree and would like to cooperate in some way, please contact us!

โœ๏ธ Citing

If you use ReinforcementLearning.jl in a scientific publication, we would appreciate references to the CITATION.bib.

โœจ Contributors

Thanks goes to these wonderful people (emoji key):


jbrea

๐Ÿ’ป ๐Ÿ“– ๐Ÿšง

Jun Tian

๐Ÿ’ป ๐Ÿ“– ๐Ÿšง ๐Ÿค”

Aman Bhatia

๐Ÿ“–

Alexander Terenin

๐Ÿ’ป

Sid-Bhatia-0

๐Ÿ’ป

norci

๐Ÿ’ป ๐Ÿšง

Sriram

๐Ÿ’ป

Pavan B Govindaraju

๐Ÿ’ป

Alex Lewandowski

๐Ÿ’ป

Raj Ghugare

๐Ÿ’ป

Roman Bange

๐Ÿ’ป

Felix Chalumeau

๐Ÿ’ป

Rishabh Varshney

๐Ÿ’ป

Zachary Sunberg

๐Ÿ’ป ๐Ÿ“– ๐Ÿšง ๐Ÿค”

Jonathan Laurent

๐Ÿค”

Andriy Drozdyuk

๐Ÿ“–

Ritchie Lee

๐Ÿ›

Xirui Zhao

๐Ÿ’ป

Nerd

๐Ÿ“–

Albin Heimerson

๐Ÿ’ป ๐Ÿ“–

michelangelo21

๐Ÿ›

GuoYu Yang

๐Ÿ“– ๐Ÿ’ป ๐Ÿ›

Prasidh Srikumar

๐Ÿ’ป

Ilan Coulon

๐Ÿ’ป

Jinrae Kim

๐Ÿ“– ๐Ÿ›

luigiannelli

๐Ÿ›

Jacob Boerma

๐Ÿ’ป

Xavier Valcarce

๐Ÿ›

Ashwani Rathee

๐Ÿ’ป

Goran Nakerst

๐Ÿ’ป

ultradian

๐Ÿ“–

Ikko Ashimine

๐Ÿ“–

Krishna Bhogaonker

๐Ÿ›

Philipp A. Kienscherf

๐Ÿ›

Stefan Krastanov

๐Ÿ“–

This project follows the all-contributors specification. Contributions of any kind welcome!