Value iteration and policy iteration are two fundamental algorithms in dynamic programming used to solve Markov Decision Processes (MDPs) in the context of reinforcement learning. Both methods aim to determine an optimal policy that maximizes the expected cumulative reward for an agent navigating through a stochastic environment. Despite their shared objective, they differ significantly in