If online reinforcement learning is used, we will be unable to evaluate the generated policy after each update, and hence will not know which policy to keep for the final evaluation.
we do a batch policy update and iterate the learning process for a number of batches, such that the data collection phase in a new iteration yields an evaluation of the policy obtained from the previous iteration at the same time.