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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>Reward Maximization - Glosarix</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="7vOKPq5xRJ"&gt;&lt;a href="https://glosarix.com/en/glossary/reward-maximization-en/"&gt;Reward Maximization&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/reward-maximization-en/embed/#?secret=7vOKPq5xRJ" width="600" height="338" title="&#x201C;Reward Maximization&#x201D; &#x2014; Glosarix" data-secret="7vOKPq5xRJ" 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: Reward maximization is a fundamental concept in reinforcement learning, an area of artificial intelligence that focuses on how agents can learn to make decisions through interaction with an environment. In this context, an agent seeks to select actions that allow it to accumulate the highest possible rewards over time. This process involves exploring different [&hellip;]</description></oembed>
