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<meta property="og:title" content="Neuroevolution of Self-Interpretable Agents" />
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<meta property="og:description" content="Evolved to focus on a fraction of its vision critical for survival." />
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<body>
<div style="text-align: center;">
<table style="width: 100%;" cellspacing="0" cellpadding="0"><tr>
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<figcaption style="text-align: left; color:#FF6C00; padding-top: 0;"><br/>Examples of evolved self-attention agents</figcaption>
<figcaption style="text-align: left; padding-top: 0;">
In this work, we evolve agents that attend to a small fraction of its visual input critical for its survival, allowing for interpretable agents that are not only compact, but also more generalizable. Here, we show examples of our agent's attention highlighted in white patches. In CarRacing (left), our agent mostly attends to the road borders, but shifts its focus to the turns before it changes heading directions. In DoomTakeCover (right), The agent is able to focus on fireballs and monsters, consistent with our intuitions.<br/>
</figcaption>
</div>
<!--In this work, we evolve agents that attend to a small fraction of its visual input critical for its survival, allowing for interpretable agents that are not only compact, but also more generalizable. Here, we show examples of our agent's attention highlighted in white patches. Note that our agent's decision-making controller receives <i>only</i> the <i>positions</i> of these white patches as visualized here, and <i>not</i> their contents. In CarRacing (left), our agent mostly attends to the road borders, but shifts its focus to the turns before it changes heading directions. In DoomTakeCover (right), The agent is able to focus on fireballs and monsters, consistent with our intuitions.<br/>-->
<!--<h2>⚠️ Draft. Please do not share this article❗️🙏🏼🙇🏻</h2>-->
<dt-byline class="l-page transparent"></dt-byline>
<h1>Neuroevolution of Self-Interpretable Agents</h1>
<p></p>
<dt-byline class="l-page" id="authors_section" hidden>
<div class="byline">
<div class="authors">
<div class="author">
<a class="name" href="https://twitter.com/yujin_tang">Yujin Tang</a>
<a class="affiliation" href="https://g.co/brain">Google Brain</a>
</div>
<div class="author">
<a class="name" href="https://twitter.com/nt_duong">Duong Nguyen</a>
<a class="affiliation" href="https://twitter.com/googlejapan">Google Japan</a>
</div>
<div class="author">
<a class="name" href="https://twitter.com/hardmaru/">David Ha</a>
<a class="affiliation" href="https://g.co/brain">Google Brain</a>
</div>
</div>
<div class="date">
<div class="month">March 18</div>
<div class="year">2020</div>
</div>
<div class="date">
<div class="month">YouTube</div>
<div class="year"><a href="https://youtu.be/DTgeEWK_Wm0" target="_blank">Talk</a></div>
</div>
<div class="date">
<div class="month">GECCO 2020<dt-fn>This work was presented at The Genetic and Evolutionary Computation Conference (<a href="https://gecco-2020.sigevo.org/index.html/HomePage" target="_blank">GECCO 2020</a>) as a full paper. It received the Best Paper Award in the EML Track.</dt-fn></div>
<div class="year" style="color: #FF6C00;"><a href="https://arxiv.org/abs/2003.08165" target="_blank">paper</a></div>
</div>
</div>
</dt-byline>