Value Iteration Algorithm for Markov Decision Processes

In this article, we discuss value iteration - a key algorithm for Markov Decision Processes (MDPs) - along with policy iteration. While both algorithms are fundamentally important, we can deepen our understanding by examining practical code implementations. Several international websites provide highly detailed and useful code repositories that demonstrate how these algorithms are implemented and executed. The value iteration algorithm typically involves initializing value functions and iteratively updating them using the Bellman optimality equation until convergence, while policy iteration alternates between policy evaluation and policy improvement steps. I recommend dedicating time to search for these code examples and study them thoroughly. This approach will significantly enhance your comprehension of both algorithms and enable more effective application in your professional work. The code often includes key functions for state transitions, reward calculations, and convergence checks, providing valuable insights into real-world MDP implementations.