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DTSTART:20240310T030000
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DTSTART:20231105T010000
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BEGIN:VEVENT
DTSTAMP:20240115T230603Z
UID:E50F5A1E-7101-4267-877F-CBF555B43E4E
DTSTART;TZID=America/Chicago:20240111T120000
DTEND;TZID=America/Chicago:20240111T130000
DESCRIPTION:Mastering effective collaboration within a team poses a formida
 ble challenge for both human and artificial intelligence entities alike. S
 triking a harmonious balance between individual objectives and collective 
 goals becomes a pivotal factor in determining the success of all stakehold
 ers involved. This presentation delves into the realm of collaborative tea
 mwork through the lens of multiagent reinforcement learning within the int
 ricate framework of the StarCraft II environment.\n\nThe presentation will
  encompass strategies for mitigating unintended consequences\, commonly re
 ferred to as specification gaming\, and elucidate how teams of AI agents c
 an leverage insights from other artificial intelligence entities to enhanc
 e their overall performance. The presentation will further provide an in-d
 epth exploration of the QMIX algorithm\, offering a comprehensive understa
 nding of its application in the context of the StarCraft II environment.\n
 \nCo-sponsored by: WIE\n\nSpeaker(s): Garrett\, \n\nBldg: 51\, Archives\, 
 Southwest Research Institute\, 229 Avenue D\, San Antonio\, Texas\, United
  States\, 78238
LOCATION:Bldg: 51\, Archives\, Southwest Research Institute\, 229 Avenue D\
 , San Antonio\, Texas\, United States\, 78238
ORGANIZER:sriram.nagaraj@swri.org
SEQUENCE:44
SUMMARY:SwRI IEEE AESS Speaker Event Talk &quot;Mitigating Unintended Consequenc
 es in Multi-Agent Reinforcement Learning&quot;
URL;VALUE=URI:https://events.vtools.ieee.org/m/398629
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;xxxxmsonormal&quot;&gt;Mastering effective 
 collaboration within a team poses a formidable challenge for both human an
 d artificial intelligence entities alike. Striking a harmonious balance be
 tween individual objectives and collective goals becomes a pivotal factor 
 in determining the success of all stakeholders involved. This presentation
  delves into the realm of collaborative teamwork through the lens of multi
 agent reinforcement learning within the intricate framework of the StarCra
 ft II environment.&lt;/p&gt;\n&lt;p class=&quot;xxxxmsonormal&quot;&gt;The presentation will enc
 ompass strategies for mitigating unintended consequences\, commonly referr
 ed to as specification gaming\, and elucidate how teams of AI agents can l
 everage insights from other artificial intelligence entities to enhance th
 eir overall performance. The presentation will further provide an in-depth
  exploration of the QMIX algorithm\, offering a comprehensive understandin
 g of its application in the context of the StarCraft II environment.&lt;/p&gt;
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