Policy iteration is a method for finding the optimal policy in reinforcement learning. It alternates between two main steps: policy evaluation and policy improvement.
In the policy evaluation step, the algorithm calculates the value function for the current policy by solving the Bellman equation. This involves computing the expected rewards from all possible actions, considering the current policy.
In the policy improvement step, the algorithm updates the policy by selecting the action that maximizes the expected return for each state based on the current value function. This process repeats, with the policy gradually improving until it converges to the optimal policy. Policy iteration is guaranteed to converge, but it can be computationally expensive, especially in large environments.