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	<title>dual-agent learning &#8211; BIOENGINEER.ORG</title>
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		<title>AI Learns to Judge Every Soccer Pass by Reading the Whole Pitch</title>
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		<pubDate>Sat, 17 Oct 2026 13:39:30 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[action valuation]]></category>
		<category><![CDATA[dual-agent learning]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[offline reinforcement learning]]></category>
		<category><![CDATA[possession value]]></category>
		<category><![CDATA[Reinforcement Learning]]></category>
		<category><![CDATA[ResNet]]></category>
		<category><![CDATA[soccer analytics]]></category>
		<category><![CDATA[spatial-temporal modeling]]></category>
		<category><![CDATA[sports data science]]></category>
		<category><![CDATA[StatsBomb]]></category>
		<category><![CDATA[Transformer]]></category>
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					<description><![CDATA[Researchers at the University of Ottawa have developed a dual-agent reinforcement learning framework that values soccer actions by modeling both teams as separate strategic agents using spatial and temporal deep learning.]]></description>
		
		
		
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