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).

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 $$\pi^*= \max(\text{all actions with discounted rewards})?$$

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).

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 $\pi^*= \max(\text{all actions with discounted rewards})$?

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).

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).

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 Policy*= Max(All actions with discounted rewards)?

I am learning about reinforcement learning and have grasped the basics of value and policy iteration, also its more helpful to grasp the intuition explained in words instead of maths if possible.

Thanks

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 $\pi^*= \max(\text{all actions with discounted rewards})$?

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).