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    <title>PyPI recent updates for gamms</title>
    <link>https://pypi.org/project/gamms/</link>
    <description>Recent updates to the Python Package Index for gamms</description>
    <language>en</language>    <item>
      <title>1.0.0</title>
      <link>https://pypi.org/project/gamms/1.0.0/</link>
      <description>GAMMS (Graph based Adversarial Multiagent Modelling Simulator) is a Python library designed for simulating large scale multi-agent scenarios on environments represented as graphs</description>
<author>rpatil@ucsd.edu, jmalegaonkar@ucsd.edu</author>      <pubDate>Tue, 09 Jun 2026 18:25:43 GMT</pubDate>
    </item>    <item>
      <title>0.2.7</title>
      <link>https://pypi.org/project/gamms/0.2.7/</link>
      <description>GAMMS (Graph based Adversarial Multiagent Modelling Simulator) is a Python library designed for simulating large scale multi-agent scenarios on environments represented as graphs</description>
<author>rpatil@ucsd.edu, jmalegaonkar@ucsd.edu</author>      <pubDate>Tue, 24 Feb 2026 19:24:19 GMT</pubDate>
    </item>    <item>
      <title>0.2.6</title>
      <link>https://pypi.org/project/gamms/0.2.6/</link>
      <description>GAMMS (Graph based Adversarial Multiagent Modelling Simulator) is a Python library designed for simulating large scale multi-agent scenarios on environments represented as graphs</description>
<author>rpatil@ucsd.edu, jmalegaonkar@ucsd.edu</author>      <pubDate>Tue, 07 Oct 2025 18:38:08 GMT</pubDate>
    </item>    <item>
      <title>0.2.5a0</title>
      <link>https://pypi.org/project/gamms/0.2.5a0/</link>
      <description>GAMMS (Graph based Adversarial Multiagent Modelling Simulator) is a Python library designed for simulating large scale multi-agent scenarios on environments represented as graphs</description>
<author>rpatil@ucsd.edu, jmalegaonkar@ucsd.edu</author>      <pubDate>Tue, 07 Oct 2025 18:26:41 GMT</pubDate>
    </item>    <item>
      <title>0.2</title>
      <link>https://pypi.org/project/gamms/0.2/</link>
      <description>None</description>
      <pubDate>Wed, 11 Jun 2025 15:29:29 GMT</pubDate>
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