# reinforcement learning: Decompose a policy gradient

I am studying the policy gradient through the website: https://towardsdatascience.com/understanding-actor-critic-methods-931b97b6df3f

Couldn't figure out how the first equation becomes the second equation?

In the second equation, why the first Expectation value have only s_0, a_0, s_1, a_1 ... s_t, a_t but no reward involved? Also, the second Expectation Value has only r_t+1, s_t+1, ... r_T, s_T, but no action involved? Could anyone please share the thoughts/intuition behind this? Thank you!

The Medium post has brackets in the wrong place . . . the second expectation must be inside the sum to make sense, otherwise $$t$$ is not defined. You can see a couple of steps later that $$Q(s_t, a_t)$$ gets magically moved back inside the sum.
I cannot see a way to fix the first expectation though that uses $$t$$ before it is defined!