Policy Iteration
Policy Iteration is a dynamic programming algorithm used in reinforcement learning and Markov decision processes to find an optimal policy that maximizes cumulative reward. It alternates between two steps: policy evaluation, which computes the value function for a given policy, and policy improvement, which updates the policy to be greedy with respect to the current value function. This iterative process converges to an optimal policy under certain conditions, such as finite state and action spaces.
Developers should learn Policy Iteration when working on problems involving sequential decision-making under uncertainty, such as robotics, game AI, or resource management systems. It is particularly useful in scenarios where the environment model (transition probabilities and rewards) is known, as it guarantees convergence to an optimal policy and serves as a foundational method for understanding more advanced reinforcement learning techniques like value iteration or Q-learning.