updates

Author: arunp77

reinforcement_learning.html

@@ -173,21 +173,13 @@ <h2>Introduction</h2>
         <h2>Core Components of Reinforcement Learning:</h2>
             <ul>
                 <li><strong>Agent:</strong> The learner or decision-maker.</li>
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                 <li><strong>Environment:</strong> Everything the agent interacts with.</li>
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                 <li><strong>State (S):</strong> A representation of the current situation.</li>
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                 <li><strong>Action (A):</strong> All possible moves the agent can take.</li>
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                 <li><strong>Reward (R):</strong> A scalar feedback signal; guides the agent.</li>
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                 <li><strong>Policy (π):</strong> Strategy used by the agent to decide actions.</li>
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                 <li><strong>Value Function (V):</strong> Predicts future rewards.</li>
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                 <li><strong>Q-Function (Q):</strong> Predicts future rewards for action-state pairs.</li>
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                 <li><strong>Model (optional):</strong> Predicts the next state and reward.</li>
             </ul>
         <h3>Types of Reinforcement Learning</h3>