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<br>
<center>
<div id="hero">
<h1>IQ-Learn: Inverse soft-Q Learning for Imitation</h1>
<div class="authors">
<table align="center" width="1030px">
<tbody>
<tr>
<td align="center" width="300px">
<center>
<span><a href="https://divyanshgarg.com/">Divyansh
Garg</a><sup>1<sup></span>
</center>
</td>
<td align="center" width="300px">
<center>
<span><a href="https://www.linkedin.com/in/shuvam-chakraborty-458631121/">Shuvam
Chakraborty</a><sup>1<sup></span>
</center>
</td>
<td align="center" width="300px">
<center>
<span><a href="https://cundy.me/">Chris
Cundy</a><sup>1<sup></span>
</center>
</td>
<td align="center" width="300px">
<center>
<span><a href="https://tsong.me/">Jiaming
Song</a><sup>1<sup></span>
</center>
</td>
<td align=" center" width="300px">
<center>
<span><a href="https://cs.stanford.edu/~ermon/">Stefano
Ermon</a><sup>1<sup></span>
</center>
</td>
</tr>
</tbody>
</table>
</div>
<table align="center" width="700px">
<tbody>
<tr>
<td align="center" width="200px">
<center>
<span style="font-size:20px">Stanford University<sup>1</sup></span>
</center>
</td>
</tr>
</tbody>
</table>
<table align="center" width="800px">
<tbody>
<tr>
<td align="center" width="150px">
<center>
<span style="font-size:25px">In NeurIPS 2021 <b><em>(Spotlight)</em></b></span>
</span>
</center>
</td>
</tr>
</tbody>
</table>
</center>
<center>
<table style="margin-top: 20px">
<tbody>
<tr>
<td>
<center><a href="https://arxiv.org/abs/2106.12142" target="_blank" class="nav-link link"><img
class="filter-blue" src="icons/paper_icon.svg" width="48" height="48"><br>Paper</a>
</center>
</td>
<td>
<center><a href="/~https://github.com/Div99/IQ-Learn" target="_blank" class="nav-link link"><img
class="filter-blue" src="icons/github.svg" width="48" height="48"><br>Code<br></a>
</center>
</td>
<td>
<center><a
href="https://slideslive.com/embed/presentation/38967041?embed_parent_url=https%3A%2F%2Fneurips.cc%2Fvirtual%2F2021%2Fposter%2F26537&embed_container_origin=https%3A%2F%2Fneurips.cc&embed_container_id=presentation-embed-38967041&auto_load=true&auto_play=false&zoom_ratio=&disable_fullscreen=false&locale=en&vertical_enabled=true&vertical_enabled_on_mobile=false&allow_hidden_controls_when_paused=true&fit_to_viewport=true&user_uuid=2f7f8b9e-d23a-478f-ad00-f0905aa4836d"
target="_blank" class="nav-link link"><img class="filter-blue" src="icons/youtube.svg"
width="48" height="48"><br>Talk<br></a></center>
</td>
</tr>
</tbody>
</table>
</center><br>
</div>
<!-- <table align="center" width="650px">
<tbody>
<tr>
<td align="center" width="150px">
<center>
<span style="font-size:20px"><a href="/~https://github.com/Div99/W-Stereo-Disp">
[GitHub]</a></span>
</center>
</td>
<td align="center" width="150px">
<center>
<span style="font-size:20px"><a href="https://slideslive.com/38937842"> [Talk]</a></span>
</center>
</td>
<td align="center" width="150px">
<center>
<span style="font-size:20px"><a href="https://arxiv.org/abs/2007.03085"> [Paper]</a></span>
</center>
</td>
<td align="center" width="150px">
<center>
<span style="font-size:20px"><a href="poster.pdf"> [Poster]</a></span>
</center>
</td>
</tr>
<tr>
</tr>
</tbody>
</table> -->
<!-- <br><br>
<hr> -->
<table align="center" width="750px">
<tbody>
<tr>
<td width="400px">
<center>
<img class="img-banner" src="teaser.gif"><br>
</center>
</td>
</tr>
<tr>
<td width=" 300px">
<center>
<div style="font-size:17px; padding-bottom: 10px">
<i>IQ-Learn reaching human performance on Atari through
pure imitation<br></i>
</div>
<span style="font-size:15px;"><i>Showing <span class=" text-primary bold">Pong</span> (Top
Left),
<span class="text-danger bold">Breakout</span> (Top Right), <span
class="text-success bold">Space
Invaders</span> (Bottom Left) ,
<span class="text-info bold">QBert</span> (Bottom Right).</i>
</span>
</center>
</td>
</tr>
</tbody>
</table>
<br>
<hr>
<center>
<h1>Abstract</h1>
</center>
<table align="center" width="850px">
<tbody>
<tr>
<td>
</td>
</tr>
</tbody>
</table>
<p class="mt-3">
In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human
or expert data is available containing useful information about the task. However, imitation learning (IL) from
a small amount of expert data can be challenging in high-dimensional environments with complex dynamics.
Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable
convergence but doesn't utilize any information involving the environment's dynamics. Many existing methods that
exploit dynamics information are difficult to train in practice due to an adversarial optimization process over
reward and policy approximators or biased, high variance gradient estimators. <br><br> We introduce a method for
dynamics-aware IL which avoids adversarial training by <b><em>learning a single Q-function</em></b>, implicitly
representing
both reward and policy. On standard benchmarks, the implicitly learned rewards show a high positive correlation
with the ground-truth rewards, illustrating our method can also be used for inverse reinforcement learning
(IRL). Our method, <span class=" text-primary"><strong>Inverse soft-Q learning (IQ-Learn)</strong></span>
obtains
<strong>state-of-the-art results</strong> in
offline and
online
imitation learning settings, significantly outperforming existing methods both in the number of required
environment interactions and scalability in high-dimensional spaces, often by more than <strong>3X</strong>.
</p>
<br><br>
<hr>
<center>
<h1>Video</h1>
</center>
<table align="center" width="1100px">
<tbody>
<tr>
</tr>
</tbody>
</table>
<table align="center" width="800px">
<tbody>
<tr>
<td align="center" width="800px">
<div id="presentation-embed-38967041" class="slp my-auto" style="width: 100%;">
<iframe
src="https://slideslive.com/embed/presentation/38967041?embed_parent_url=https%3A%2F%2Fneurips.cc%2Fvirtual%2F2021%2Fposter%2F26537&embed_container_origin=https%3A%2F%2Fneurips.cc&embed_container_id=presentation-embed-38967041&auto_load=true&auto_play=false&zoom_ratio=&disable_fullscreen=false&locale=en&vertical_enabled=true&vertical_enabled_on_mobile=false&allow_hidden_controls_when_paused=true&fit_to_viewport=true&user_uuid=2f7f8b9e-d23a-478f-ad00-f0905aa4836d"
height="564" scrolling="no" frameborder="0"
sandbox="allow-forms allow-pointer-lock allow-popups allow-same-origin allow-scripts allow-top-navigation"
allow="autoplay; fullscreen" allowfullscreen="" webkitallowfullscreen=""
mozallowfullscreen="" style="margin: 0px auto; display: block; width: 100%;"></iframe>
</div>
</td>
</tr>
</tbody>
</table>
<hr>
<center>
<h1>Approach</h1>
</center>
<table align="center" width="600px">
<tbody>
<tr>
<td align="center"><a href="/~https://github.com/Div99/IQ-Learn"><img class="round"
style="height:1000px; margin-left: 60px" src="approach.png"></a></td>
<!-- </br> -->
</tr>
</tbody>
</table>
<center> <br>
<!-- <span style="font-size:28px">Code coming soon!</span></i> -->
<span style="font-size:24px"> <a href="/~https://github.com/Div99/IQ-Learn">[GitHub]</a>
</span><i></i>
<span style="font-size:28px"></span>
<br>
</center>
<table align="center" width="800px">
<tbody>
<tr></tr>
</tbody>
</table>
<br>
<hr>
<!-- <table align=center width=550px> -->
<center>
<h1>Recovering Rewards</h1>
</center>
<br>
<table align="center" width="600px">
<tbody>
<tr>
<td align="center"><a href="grid.pdf"><img class="round" style="height:300px" src="grid.png"></a></td>
<!-- </br> -->
<!-- </br> -->
</tr>
</tbody>
</table>
<br>
<center>
<span style="font-size:14pt">
Recovering environment rewards on a discrete GridWorld environment with 5 possible actions: <span
class="text-success">up, down,
left, right, stay</span>
</span>
</center>
<br>
<hr>
<center>
<h1>Paper</h1>
</center>
<table align="center" width="600px">
<tbody>
<tr>
<td align="center"><a href="https://arxiv.org/abs/2106.12142"><img style="height:350px"
src="paper_thumb.png"></a></td>
</tr>
</tbody>
</table>
<br>
<table align="center" width="500px">
<tbody>
<tr>
<td><span style="font-size:24px">
<center>
<a
href="https://proceedings.neurips.cc/paper/2021/file/210f760a89db30aa72ca258a3483cc7f-Paper.pdf">[Paper]</a>
</center>
</span></td>
<td><span style="font-size:24px">
<center>
<a
href="https://proceedings.neurips.cc/paper/2021/file/210f760a89db30aa72ca258a3483cc7f-Supplemental.pdf">[Suppl]</a>
</center>
</span></td>
<td><span style="font-size:24px">
<center>
<a href="neurips_2021_iq.txt">[Bibtex]</a>
</center>
</span></td>
</tr>
</tbody>
</table>
<br>
<hr>
<center>
<h1>Poster</h1>
</center>
<table align="center" width="600px">
<br>
<tbody>
<tr>
<td align="center"><a href="poster.pdf"><img class="paper-big" style="height:650px"
src="poster.jpg"></a></td>
</tr>
</tbody>
</table>
<br>
<hr>
<center>
<h1>Citation</h1>
</center>
<table align="center" width="1000px">
<tbody>
<tr>
<td><span style="font-size:14pt">
</span>
</td>
</tr>
</tbody>
</table>
<pre>
@inproceedings{
garg2021iqlearn,
title={IQ-Learn: Inverse soft-Q Learning for Imitation},
author={Divyansh Garg and Shuvam Chakraborty and Chris Cundy and Jiaming Song and Stefano Ermon},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=Aeo-xqtb5p}
}
</pre>
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