Policy evaluation and policy improvement are two distinct steps in reinforcement learning algorithms like policy iteration. Policy evaluation calculates how effective a given policy is, while policy improvement updates the policy to make better decisions based on that evaluation. Together, these steps form a cycle that iteratively refines an agent’s behavior.
Policy evaluation focuses on estimating the value of states (or state-action pairs) under a specific policy. For example, if an agent follows a policy that dictates movement in a grid world, policy evaluation computes the expected long-term rewards for each state, such as the value of being near a goal versus a hazard. This is typically done using algorithms like iterative policy evaluation, which repeatedly applies the Bellman equation to update state values until they stabilize. The result is a value function that quantifies the policy’s performance but does not change the policy itself. For instance, in a game-playing scenario, this step answers the question, “How good is my current strategy?” but not “How do I improve it?”
Policy improvement, on the other hand, uses the value function from policy evaluation to create a better policy. If the evaluation reveals that a certain action in a state yields a higher expected reward than the current policy’s choice, the policy is updated to favor that action. For example, in a self-driving car simulation, if the evaluation shows that braking earlier in a specific scenario reduces collisions, the policy is adjusted to prioritize braking. This step often employs a greedy approach, selecting actions with the highest estimated value. However, improvements can also balance exploration (trying new actions) and exploitation (sticking to known good actions) to avoid local optima.
The two steps are interdependent. Policy evaluation provides the data needed for improvement, while policy improvement generates new policies to evaluate. In practice, algorithms like policy iteration alternate between them until the policy converges to an optimal solution. For example, in a warehouse robot pathfinding task, evaluation might reveal bottlenecks in the current routing strategy, and improvement could reroute the robot to faster paths. This iterative process ensures that the agent’s behavior becomes increasingly effective over time, leveraging evaluation to measure progress and improvement to drive change.