Home Projects ReinforcementLearning.jl
ReinforcementLearning.jl
Julia

ReinforcementLearning.jl

A reinforcement learning package for Julia

by JuliaReinforcementLearning · GitHub
Stars
Forks
Created
Last commit
Julia
Language
deep-q-networkdeep-reinforcement-learningjuliaJulia
View on GitHub
In plain words

Run experiments and compare different reinforcement learning algorithms using Julia programming language.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
ReinforcementLearning.jl — GitHub preview card
📈 Star history
651650
2026-07-202026-08-31
📈 Track ReinforcementLearning.jl

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

A reinforcement learning package for Julia

ReinforcementLearning.jl has 650 stars on GitHub. It has been forked 109 times. ReinforcementLearning.jl is written mainly in Julia. It has been in active development since 2018. Its main topics are deep-q-network, deep-reinforcement-learning, julia, machine-learning.

Frequently asked questions

What is ReinforcementLearning.jl?

A reinforcement learning package for Julia

Is ReinforcementLearning.jl open source?

ReinforcementLearning.jl is an open-source project.

Is ReinforcementLearning.jl free?

Yes. ReinforcementLearning.jl is free and open source — you can use, modify and self-host it.

What language is ReinforcementLearning.jl written in?

ReinforcementLearning.jl is written mainly in Julia.

🏅 Maintainer of this project?
olud.ai badge — ReinforcementLearning.jl

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=juliareinforcementlearning-reinforcementlearning-jl)](https://olud.ai/project/juliareinforcementlearning-reinforcementlearning-jl.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.