Value Iteration
Value Iteration is a dynamic programming algorithm used in reinforcement learning and Markov decision processes (MDPs) to compute an optimal policy by iteratively improving value estimates for states. It works by repeatedly applying the Bellman optimality equation to update state values until convergence, ultimately deriving the policy that maximizes expected cumulative reward. This method is foundational for solving problems where an agent must make sequential decisions under uncertainty.
Developers should learn Value Iteration when working on reinforcement learning applications, such as robotics, game AI, or autonomous systems, where optimal decision-making in stochastic environments is required. It is particularly useful for problems with known transition dynamics and rewards, providing a guaranteed convergence to the optimal policy, making it essential for academic research and practical implementations in controlled settings.