# Policy gradient base line function

On the bottom of page ten of the following paper on probabilistic reinforcement learning, there are 3 equations where is author manipulates the policy gradient $$\nabla_\theta J(\theta)$$.

Can someone please explain to me how to derive the last (third) line from the previous (second) line?

I feel like we have to prove either one of those expressions: But I don't know how to go about it.

$$\nabla_\theta log\ q_\theta (a_t|s_t) (\sum_{t'=t}^T b(s_{t'})) = 0$$ or $$E_{(s_t,a_t) ~ q(s_t,a_t)}[\nabla_\theta log\ q_\theta (a_t|s_t) (\sum_{t'=t}^T b(s_{t'}))] = 0$$