https://arshadhs.github.io/tags/monte-carlo-methods/Recent content in Monte Carlo Methods on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/050-monte-carlo-methods/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/050-monte-carlo-methods/<h1 id="monte-carlo-methods"> Monte Carlo Methods <a class="anchor" href="#monte-carlo-methods">#</a> </h1> <p>Monte Carlo (MC) methods learn value functions and policies by averaging returns observed in complete episodes. Unlike Dynamic Programming, they do not require transition probabilities or a complete model of the environment.</p> <span style="color: blue;"> <strong>Monte Carlo learning replaces model-based expectation with averages from sampled experience.</strong> </span> <hr> <h2 id="1-the-main-idea-"> 1. The Main Idea ☆ <a class="anchor" href="#1-the-main-idea-">#</a> </h2> <p>Suppose the agent repeatedly reaches a state <span> \( s \) </span> . Each time, it records the return received from that point until the episode ends. The average of these returns estimates the state&rsquo;s value.</p>