https://arshadhs.github.io/tags/dynamic-programming/Recent content in Dynamic Programming on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/040-dynamic-programming/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/040-dynamic-programming/<h1 id="dynamic-programming"> Dynamic Programming <a class="anchor" href="#dynamic-programming">#</a> </h1> <p>Dynamic Programming (DP) solves a reinforcement learning problem by repeatedly applying Bellman equations. It uses a complete model of the environment to calculate value functions and improve a policy.</p> <span style="color: blue;"> <strong>Dynamic Programming turns a known model of an MDP into a policy for choosing actions.</strong> </span> <ul> <li>Dynamic Programming</li> <li>Policy Iteration</li> <li>Value Iteration</li> <li>Generalized policy iteration</li> <li>Efficiency of Dynamic Programming</li> </ul> <hr> <h2 id="1-when-dynamic-programming-can-be-used-"> 1. When Dynamic Programming Can Be Used ☆ <a class="anchor" href="#1-when-dynamic-programming-can-be-used-">#</a> </h2> <p>DP assumes that the environment dynamics are known. For every state and action, the agent needs the probabilities of possible next states and rewards:</p>