https://arshadhs.github.io/tags/temporal-difference-learning/Recent content in Temporal-Difference Learning on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/060-temporal-difference-learning-i/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/060-temporal-difference-learning-i/<h1 id="temporal-difference-learning-i"> Temporal-Difference Learning I <a class="anchor" href="#temporal-difference-learning-i">#</a> </h1> <p>Temporal-Difference (TD) learning updates predictions from one transition at a time. It learns directly from experience like Monte Carlo methods, but it does not need to wait until the episode ends.</p> <p><strong>Course Content covered in this module:</strong></p> <ul> <li>Temporal-Difference Learning</li> <li>TD(0), SARSA, Q-Learning and Expected SARSA</li> </ul> <span style="color: blue;"> <strong>TD learning updates an estimate using an immediate reward and another current estimate.</strong> </span> <hr> <h2 id="learning-objectives"> Learning Objectives <a class="anchor" href="#learning-objectives">#</a> </h2> <p>By the end of this module, you should be able to:</p>https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/070-temporal-difference-learning-ii/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/070-temporal-difference-learning-ii/<h1 id="temporal-difference-learning-ii-and-drl-taxonomy"> Temporal-Difference Learning II and DRL Taxonomy <a class="anchor" href="#temporal-difference-learning-ii-and-drl-taxonomy">#</a> </h1> <p>One-step TD methods bootstrap after a single transition. Multi-step methods instead use several observed rewards before bootstrapping, forming a bridge between TD(0) and Monte Carlo learning.</p> <p><strong>Course Content covered in this module:</strong></p> <ul> <li>Temporal-Difference Learning: n-step returns and TD(λ)</li> <li>Classification of Reinforcement Learning approaches, algorithms and applications: <ul> <li>Model-Based versus Model-Free</li> <li>Value-Based versus Policy-Based</li> <li>On-Policy versus Off-Policy</li> </ul> </li> </ul> <span style="color: blue;"> <strong>The number of sampled steps controls how far learning looks ahead before it bootstraps.</strong> </span> <hr> <h2 id="learning-objectives"> Learning Objectives <a class="anchor" href="#learning-objectives">#</a> </h2> <p>By the end of this module, you should be able to:</p>