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<oembed><version>1.0</version><provider_name>Glosarix</provider_name><provider_url>https://glosarix.com/en/</provider_url><author_name>Team Glosarix</author_name><author_url>https://glosarix.com/en/author/adm_glosarix/</author_url><title>Reinforcement Strategy - Glosarix</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="Rcct9INjAC"&gt;&lt;a href="https://glosarix.com/en/glossary/reinforcement-strategy-en/"&gt;Reinforcement Strategy&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/reinforcement-strategy-en/embed/#?secret=Rcct9INjAC" width="600" height="338" title="&#x201C;Reinforcement Strategy&#x201D; &#x2014; Glosarix" data-secret="Rcct9INjAC" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>Description: The reinforcement strategy is an approach used in reinforcement learning, an area of artificial intelligence that focuses on how agents should make decisions in an environment to maximize cumulative reward. In this context, the reinforcement strategy involves implementing a plan or method that guides the agent in selecting actions based on the rewards received [&hellip;]</description></oembed>
