I have a hard time grasping the need for policy optimization and say the log kernel trick/score function. Instead of using the score function, why do you not simply optimize for the highest reward and choose $$4\pi^*= \max(\text{all actions with discounted rewards})?$$

I am learning about reinforcement learning and have grasped the basics of value and policy iteration. I would appreciate if answers are intuitive (without math, if possible).