Description: Policy iteration is a fundamental approach in reinforcement learning that focuses on optimizing policies through a cyclical process. This algorithm alternates between two key stages: policy evaluation and policy improvement. In the policy evaluation stage, the value of a given policy is estimated, which involves calculating the expected future rewards that can be obtained by following that policy. This stage allows understanding how effective the current policy is in terms of maximizing rewards. On the other hand, policy improvement uses the information obtained in the evaluation to adjust the policy, seeking a version that offers a higher expected value. This cycle is repeated until an optimal policy is reached, meaning a policy that cannot be improved without changing the actions taken. Policy iteration is particularly relevant in environments where decisions must be made sequentially and where the consequences of actions may not be immediate. Its ability to converge towards optimal solutions makes it a powerful tool in the field of machine learning and artificial intelligence, where the goal is to maximize performance in complex and dynamic tasks.
