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<!doctype html>
<html lang="en">
<head>
{{ insert gtag.html }}
<title>JuliaReinforcementLearning</title>
<link rel="icon" href="/assets/site/logo.svg">
<link rel="stylesheet" href="/css/custom.css" />
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css"
integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">
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<body>
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<div class="jumbotron jumbotron-fluid page-heading text-center">
<div class="container">
<img src="/assets/site/logo.svg" width="250px">
<h1> ReinforcementLearning.jl </h1>
<p class="text-muted"> A collection of tools for doing reinforcement learning research in Julia. </p>
<p>
<a class="github-button" href="https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl"
data-icon="octicon-star" data-size="large" data-show-count="true"
aria-label="Star JuliaLang/julia on GitHub">Star Us</a>
</p>
</div>
</div>
<hr>
<div class="container">
<div class="row key-feature">
<div class="col-md-12">
<h3 class="text-muted">Key Features &<br>Capabilities</h3>
</div>
</div>
<div class="row">
<div class="col-lg-3 col-md-6 col-sm-12 key-feature-box">
<h5>Easy experimentation</h5>
<p>Make it easy for new users to run benchmark experiments, compare different algorithms, evaluate and
diagnose agents.</p>
</div>
<div class="col-lg-3 col-md-6 col-sm-12 key-feature-box">
<h5>Reproducibility</h5>
<p>Facilitate reproducibility from traditional tabular methods to modern deep reinforcement learning
algorithms.</p>
</div>
<div class="col-lg-3 col-md-6 col-sm-12 key-feature-box">
<h5>Reusability and extensibility</h5>
<p>Provide elaborately designed components and interfaces to help users implement new algorithms.</p>
</div>
<div class="col-lg-3 col-md-6 col-sm-12 key-feature-box">
<h5>Feature-rich Environments</h5>
<p>A number of built-in environments and third-party environment wrappers are provided to evaluate
algorithms in various scenarios.</p>
</div>
</div>
</div>
<hr>
<div class="container">
<div class="row feature-section">
<div class="col-lg-8 col-md-6 ">
<div class="row">
<div class="col-lg-1 col-md-0">
</div>
<div class="col-lg-10 col-md-12 text-center">
<h3>Get Started in 3 lines!</h3>
<p>ReinforcementLearning.jl is a wrapper package which contains a collection of different packages in
the JuliaReinforcementLearning organization. You can simply run many built-in experiments in 3
lines.</p>
<a type="button" href="/get_started/" class="btn btn-sm btn-outline-secondary">Get Started!</a>
</div>
<div class="col-lg-1 col-md-0">
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 mt-auto mb-auto">
<!-- HTML generated using hilite.me -->
<div style="background: #272822; overflow:auto;width:auto;padding:.8em .8em;border-radius:15px">
<pre style="margin: 0; line-height: 125%"><span style="color: #06a313">julia></span> <span style="color: #f8f8f2">]</span> <span style="color: #f8f8f2">add</span> <span style="color: #f8f8f2">ReinforcementLearning</span>
<span style="color: #06a313">julia></span> <span style="color: #66d9ef">using</span> <span style="color: #f8f8f2">ReinforcementLearning</span>
<span style="color: #06a313">julia></span> <span style="color: #66d9ef">run</span><span style="color: #f8f8f2">(</span><span style="color: #ee18ee">E</span><span style="color: #e6db74">`JuliaRL_BasicDQN_CartPole`</span><span style="color: #f8f8f2">)</span>
</pre>
</div>
</div>
</div>
</div>
<hr>
<div class="container">
<div class="row feature-section text-center">
<div class="col-lg-4 col-md-6">
<img src="/assets/site/RLIntro2Cover-min.jpg">
</div>
<div class="col-lg-8 col-md-6 mt-auto mb-auto">
<div class="row">
<div class="col-lg-1 col-md-0"></div>
<div class="col-lg-10 col-md-12">
<h3>
Tabular Reinforcement Learning
</h3>
<p>
In ReinforcementLearningAnIntroduction.jl, we reproduced most figures in the famous book: <span
style="font-style:italic">Reinforcement Learning: An Introduction (Second Edition)</span>.
You can try those examples interactively online with the help of MyBinder and learn many tabular
reinforcement learning algorithms.
</p>
<a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningAnIntroduction.jl" type="button" class="btn btn-sm btn-outline-secondary">Learn Now!</a>
</div>
<div class="col-lg-1 col-md-0"></div>
</div>
</div>
</div>
</div>
<hr>
<div class="container">
<div class="row feature-section text-center">
<div class="col-lg-8 col-md-6 mt-auto mb-auto">
<div class="row">
<div class="col-lg-1 col-md-0"></div>
<div class="col-lg-10 col-md-12">
<h3>
Deep Reinforcement Learning
</h3>
<p>
In ReinforcementLearningZoo.jl, many deep reinforcement learning algorithms are implemented,
including DQN, C51, Rainbow, IQN, A2C, PPO, DDPG, etc. All algorithms are written in a composable
way, which make them easy to read, understand and extend.
</p>
<a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearningZoo.jl" type="button" class="btn btn-sm btn-outline-secondary">Try Now!</a>
</div>
<div class="col-lg-1 col-md-0"></div>
</div>
</div>
<div class="col-lg-4 col-md-6">
<img src="/assets/site/RLZoo.svg">
</div>
</div>
</div>
<hr>
<div class="container">
<div class="row">
<div class="col-md-12">
<h2>Community</h2>
</div>
<div class="col-md-12">
<p class="h2-subheadline">Join the Julia Reinforcement Learning community to learn, contribute, and get
your questions answered.</p>
</div>
</div>
<div class="row">
<div class="col-lg-4 col-md-12">
<div class="card">
<div class="card-body">
<h5 class="card-title"> <svg class="octicon octicon-mark-github v-align-middle" height="32"
viewBox="0 0 16 16" version="1.1" width="32" aria-hidden="true">
<path fill-rule="evenodd"
d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z">
</path>
</svg> Github Issue/Pull Request</h5>
<p class="card-text">Ask package usage questions, discuss designs and propose new features
through github issues. Contributions through pull requests are warmly welcomed!</p>
<a type="button" href="https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/issues" class="btn btn-sm btn-outline-secondary">Create an issue!</a>
<a href="https://github.com/JuliaReinforcementLearning/ReinforcementLearning.jl/pulls" type="button" class="btn btn-sm btn-outline-secondary">Make a PR!</a>
</div>
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<h5 class="card-title"><svg xmlns="http://www.w3.org/2000/svg" height="32"
viewBox="0 -1 104 106">
<defs>
<style>
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.cls-3 {
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.cls-4 {
fill: #00a94f;
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.cls-5 {
fill: #f15d22;
}
.cls-6 {
fill: #e31b23;
}
</style>
</defs>
<title>Discourse_logo</title>
<g id="Layer_2" data-name="Layer 2">
<g id="Layer_3" data-name="Layer 3">
<path class="cls-1"
d="M51.87,0C23.71,0,0,22.83,0,51c0,.91,0,52.81,0,52.81l51.86-.05c28.16,0,51-23.71,51-51.87S80,0,51.87,0Z" />
<path class="cls-2"
d="M52.37,19.74A31.62,31.62,0,0,0,24.58,66.41l-5.72,18.4L39.4,80.17a31.61,31.61,0,1,0,13-60.43Z" />
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d="M77.45,32.12a31.6,31.6,0,0,1-38.05,48L18.86,84.82l20.91-2.47A31.6,31.6,0,0,0,77.45,32.12Z" />
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d="M71.63,26.29A31.6,31.6,0,0,1,38.8,78L18.86,84.82,39.4,80.17A31.6,31.6,0,0,0,71.63,26.29Z" />
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</g>
</g>
</svg> Julia Discourse/Slack <svg height="32"
enable-background="new 0 0 2447.6 2452.5" viewBox="0 0 2447.6 2452.5"
xmlns="http://www.w3.org/2000/svg">
<g clip-rule="evenodd" fill-rule="evenodd">
<path
d="m897.4 0c-135.3.1-244.8 109.9-244.7 245.2-.1 135.3 109.5 245.1 244.8 245.2h244.8v-245.1c.1-135.3-109.5-245.1-244.9-245.3.1 0 .1 0 0 0m0 654h-652.6c-135.3.1-244.9 109.9-244.8 245.2-.2 135.3 109.4 245.1 244.7 245.3h652.7c135.3-.1 244.9-109.9 244.8-245.2.1-135.4-109.5-245.2-244.8-245.3z"
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</svg></h5>
<p class="card-text">Ask general reinforcement learning related questions on Julia discourse in
the #machinelearning domain, or on the Slack in #reinforcement-learnin channel.</p>
<a href="https://discourse.julialang.org/c/domain/ML/24" type="button" class="btn btn-sm btn-outline-secondary">Ask on Discourse!</a>
<a href="https://slackinvite.julialang.org/" type="button" class="btn btn-sm btn-outline-secondary">Join Julia Slack!</a>
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xmlns:xlink="http://www.w3.org/1999/xlink" height="30" viewBox="0 0 325 300"
version="1.1">
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style=" stroke:none;fill-rule:nonzero;fill:rgb(79.6%,23.5%,20%);fill-opacity:1;"
d="M 150.898438 225 C 150.898438 266.421875 117.320312 300 75.898438 300 C 34.476562 300 0.898438 266.421875 0.898438 225 C 0.898438 183.578125 34.476562 150 75.898438 150 C 117.320312 150 150.898438 183.578125 150.898438 225 " />
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</g>
</svg> Other Julia Packages</h5>
<p class="card-text">
<ul class="package-list">
<li><a href="https://github.com/jonathan-laurent/AlphaZero.jl">AlphaZero.jl</a></li>
<li><a href="https://github.com/JuliaPOMDP/DeepQLearning.jl">DeepQLearning.jl</a></li>
<li><a href="https://github.com/mkschleg/DeepRL.jl">DeepRL.jl</a></li>
<li><a href="https://github.com/JuliaML/Reinforce.jl">Reinforce.jl</a></li>
</ul>
</p>
<a href="https://juliahub.com/ui/Packages?q=reinforce" type="button" class="btn btn-sm btn-outline-secondary">Find more packages on JuliaHub!</a>
</div>
</div>
</div>
</div>
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