Policy evaluation and policy improvement are two key components in the realm of reinforcement learning, particularly within the context of the policy iteration framework. Policy evaluation involves assessing a given policy to determine how well it performs in a specific environment. This is typically done by calculating the expected return or value for each state when following that policy. For example, if you have a policy that dictates how a robot should move in a maze, policy evaluation would involve simulating the robot's movements to see how quickly it can reach the goal. The result is a value function that summarizes the effectiveness of that policy across all states.
On the other hand, policy improvement is the process of refining a policy based on the information gathered during the evaluation phase. After assessing the current policy, developers can identify which actions lead to better results and adjust the policy accordingly. In our robot maze example, if the evaluation shows that certain paths consistently lead to longer travel times, the policy improvement step would involve altering the robot's decision-making to favor the more efficient paths. This results in a new policy that is expected to yield a better performance than the original.
Together, these two processes form an iterative cycle. You evaluate a policy to understand its strengths and weaknesses, and then you improve it based on that understanding. This cycle continues until the policy reaches a level of performance that is satisfactory or optimal. In practical terms, developers can think of policy evaluation as gathering data to inform decisions, while policy improvement is the act of applying those insights to create a more effective solution. This iterative approach is fundamental in achieving better results in applications like game AI, robotics, and any scenario where decision-making is critical.