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<!-- {% extends "layout.html" %} -->
{% extends "Test-layout.html" %}



{% block content %}
<h1 class="text-4xl font-bold text-gray-800 mb-6">Reinforcement Learning</h1>
<p class="text-gray-600 text-lg mb-8">Reinforcement learning (RL) is a subfield of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some cumulative reward.</p>
<div class="flex flex-col gap-6">
    <div class="card p-6">
        <h2 class="text-2xl font-semibold text-gray-800 mb-4">Core Concepts of RL</h2>
        <div class="flex flex-wrap gap-4">
            <a href="/agent-environment-state-test" class="algorithm-box">Agent, Environment, and State</a>
            <a href="/action-policy-test" class="algorithm-box">Action and Policy</a>
            <a href="/reward-value-function-test" class="algorithm-box">Reward and Value Function</a>
            <a href="/q-learning-test" class="algorithm-box">Q-Learning</a>
            <a href="/deep-reinforcement-learning-test" class="algorithm-box">Deep Reinforcement Learning</a>
        </div>
    </div>
</div>
{% endblock %}