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UT-Austin-RPL · Nov 3, 2023 · b355caa · b355caa
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diff --git a/_site/feed.xml b/_site/feed.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.2">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2023-11-02T22:44:28-05:00</updated><id>http://localhost:4000/feed.xml</id><title type="html">AMAGO</title><subtitle>A simple and scalable agent for sequence-based RL</subtitle></feed>
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="4.2.2">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2023-11-02T23:11:02-05:00</updated><id>http://localhost:4000/feed.xml</id><title type="html">AMAGO</title><subtitle>A simple and scalable agent for sequence-based RL</subtitle></feed>
diff --git a/_site/index.html b/_site/index.html
@@ -147,7 +147,7 @@
 </style>
 
 <div id="primarycontent">
-<center><h1><strong>AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents</strong></h1></center>
+<center><h1><strong>AMAGO: Scalable In-Context Reinforcement Learning <br />for Adaptive Agents</strong></h1></center>
 <center><h2>
     <a href="https://jakegrigsby.github.io">Jake Grigsby<sup>1</sup></a>&nbsp;&nbsp;&nbsp;
     <a href="https://jimfan.me">Jim Fan<sup>2</sup></a>&nbsp;&nbsp;&nbsp;
@@ -193,7 +193,7 @@ <h4>
 </h4>
 
 <div class="container">
-	  <a href="./src/figure/fig1_iclr_e_notation.pdf"><img src="./src/figure/fig1_iclr_e_notation.pdf" style="width:100%;" /> </a>
+	  <a href="./src/figure/fig1_iclr_e_notation.png"><img src="./src/figure/fig1_iclr_e_notation.png" style="width:100%;" /> </a>
 </div>
 
 
@@ -217,7 +217,7 @@ <h4>
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/popgym_summary_expanded_outliers.pdf"><img src="./src/figure/popgym_summary_expanded_outliers.pdf" style="width:100%;" /> </a>
+	 <a href="./src/figure/popgym_summary_expanded_outliers.png"><img src="./src/figure/popgym_summary_expanded_outliers.png" style="width:100%;" /> </a>
    </tr>
   </tbody>
 </table>
@@ -232,7 +232,7 @@ <h4>
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/case_studies_arxiv_v2.pdf"><img src="./src/figure/case_studies_arxiv_v2.pdf" style="width:100%;" /> </a>
+	 <a href="./src/figure/case_studies_arxiv_v2.png"><img src="./src/figure/case_studies_arxiv_v2.png" style="width:100%;" /> </a>
    </tr>
   </tbody>
 </table>
@@ -259,7 +259,7 @@ <h4>
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/crafter_condensed_results.pdf"><img src="./src/figure/crafter_condensed_results.pdf" style="width:100%;" /> </a>
+	 <a href="./src/figure/crafter_condensed_results.png"><img src="./src/figure/crafter_condensed_results.png" style="width:100%;" /> </a>
    </tr>
   </tbody>
 </table>
@@ -318,7 +318,7 @@ <h4>
 In-context RL is applicable to any memory, generalization, or meta-learning problem, and we have designed AMAGO to be flexible enough to support all of these cases. Our code is fully open-source and includes examples of how to apply AMAGO to new domains. We hope our agent can serve as a strong baseline in the development of new benchmarks that require long-term memory and adaptation. <a href="https://github.com/UT-Austin-RPL/amago">Check it out on GitHub here</a>.
 </h4>
 
-<a href="https://github.com/UT-Austin-RPL/amago"><img src="./src/logos/rpl_logo.pdf" style="width:30%;" /> </a>
+<a href="https://github.com/UT-Austin-RPL/amago"><img src="./src/logos/rpl_logo.png" style="width:30%;" /> </a>
 
 
 

diff --git a/_site/src/figure/case_studies_arxiv_v2.pdf b/_site/src/figure/case_studies_arxiv_v2.pdf
diff --git a/_site/src/figure/case_studies_arxiv_v2.png b/_site/src/figure/case_studies_arxiv_v2.png
diff --git a/_site/src/figure/crafter_condensed_results.pdf b/_site/src/figure/crafter_condensed_results.pdf
diff --git a/_site/src/figure/crafter_condensed_results.png b/_site/src/figure/crafter_condensed_results.png
diff --git a/_site/src/figure/fig1_iclr_e_notation.pdf b/_site/src/figure/fig1_iclr_e_notation.pdf
diff --git a/_site/src/figure/fig1_iclr_e_notation.png b/_site/src/figure/fig1_iclr_e_notation.png
diff --git a/_site/src/figure/popgym_summary_expanded_outliers.pdf b/_site/src/figure/popgym_summary_expanded_outliers.pdf
diff --git a/_site/src/figure/popgym_summary_expanded_outliers.png b/_site/src/figure/popgym_summary_expanded_outliers.png
diff --git a/_site/src/logos/rpl_logo.pdf b/_site/src/logos/rpl_logo.pdf
diff --git a/_site/src/logos/rpl_logo.png b/_site/src/logos/rpl_logo.png
diff --git a/index.markdown b/index.markdown
@@ -158,7 +158,7 @@ highlight {
 </style>
 
 <div id="primarycontent">
-<center><h1><strong>AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents</strong></h1></center>
+<center><h1><strong>AMAGO: Scalable In-Context Reinforcement Learning <br>for Adaptive Agents</strong></h1></center>
 <center><h2>
     <a href="https://jakegrigsby.github.io">Jake Grigsby<sup>1</sup></a>&nbsp;&nbsp;&nbsp;
     <a href="https://jimfan.me">Jim Fan<sup>2</sup></a>&nbsp;&nbsp;&nbsp;
@@ -204,7 +204,7 @@ AMAGO improves memory and adaptation by optimizing long-context Transformers on
 </h4>
 
 <div class="container">
-	  <a href="./src/figure/fig1_iclr_e_notation.pdf"><img src="./src/figure/fig1_iclr_e_notation.pdf" style="width:100%;"> </a>
+	  <a href="./src/figure/fig1_iclr_e_notation.png"><img src="./src/figure/fig1_iclr_e_notation.png" style="width:100%;"> </a>
 </div>
 
 
@@ -228,7 +228,7 @@ In-Context RL's flexibility lets us evaluate AMAGO on many generalization, memor
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/popgym_summary_expanded_outliers.pdf"><img src="./src/figure/popgym_summary_expanded_outliers.pdf" style="width:100%;"> </a>
+	 <a href="./src/figure/popgym_summary_expanded_outliers.png"><img src="./src/figure/popgym_summary_expanded_outliers.png" style="width:100%;"> </a>
    </tr>
   </tbody>
 </table>
@@ -243,7 +243,7 @@ AMAGO handles meta-learning as a simple extension of zero-shot generalization, a
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/case_studies_arxiv_v2.pdf"><img src="./src/figure/case_studies_arxiv_v2.pdf" style="width:100%;"> </a>
+	 <a href="./src/figure/case_studies_arxiv_v2.png"><img src="./src/figure/case_studies_arxiv_v2.png" style="width:100%;"> </a>
    </tr>
   </tbody>
 </table>
@@ -270,7 +270,7 @@ As an example, we evaluate instruction-conditioned agents in the procedurally ge
    <tr>
 	<td align="center" valign="middle">
     </td>
-	 <a href="./src/figure/crafter_condensed_results.pdf"><img src="./src/figure/crafter_condensed_results.pdf" style="width:100%;"> </a>
+	 <a href="./src/figure/crafter_condensed_results.png"><img src="./src/figure/crafter_condensed_results.png" style="width:100%;"> </a>
    </tr>
   </tbody>
 </table>
@@ -329,7 +329,7 @@ Above, we use several single-task instructions to evaluate the exploration capab
 In-context RL is applicable to any memory, generalization, or meta-learning problem, and we have designed AMAGO to be flexible enough to support all of these cases. Our code is fully open-source and includes examples of how to apply AMAGO to new domains. We hope our agent can serve as a strong baseline in the development of new benchmarks that require long-term memory and adaptation. <a href="https://github.com/UT-Austin-RPL/amago">Check it out on GitHub here</a>.
 </h4>
 
-<a href="https://github.com/UT-Austin-RPL/amago"><img src="./src/logos/rpl_logo.pdf" style="width:30%;"> </a>
+<a href="https://github.com/UT-Austin-RPL/amago"><img src="./src/logos/rpl_logo.png" style="width:30%;"> </a>
 
 
 

diff --git a/src/figure/case_studies_arxiv_v2.pdf b/src/figure/case_studies_arxiv_v2.pdf
diff --git a/src/figure/case_studies_arxiv_v2.png b/src/figure/case_studies_arxiv_v2.png
diff --git a/src/figure/crafter_condensed_results.pdf b/src/figure/crafter_condensed_results.pdf
diff --git a/src/figure/crafter_condensed_results.png b/src/figure/crafter_condensed_results.png
diff --git a/src/figure/fig1_iclr_e_notation.pdf b/src/figure/fig1_iclr_e_notation.pdf
diff --git a/src/figure/fig1_iclr_e_notation.png b/src/figure/fig1_iclr_e_notation.png
diff --git a/src/figure/popgym_summary_expanded_outliers.pdf b/src/figure/popgym_summary_expanded_outliers.pdf
diff --git a/src/figure/popgym_summary_expanded_outliers.png b/src/figure/popgym_summary_expanded_outliers.png
diff --git a/src/logos/rpl_logo.pdf b/src/logos/rpl_logo.pdf
diff --git a/src/logos/rpl_logo.png b/src/logos/rpl_logo.png