If I got it correctly, for Vanilla Policy Gradient which updates only once for each trajectory rather than for each step in REINFORCE, we have a sum up the log-probability of policy multiplied by advantage estimate for each time step, then compute a single objective of type Variable with calling .backward() to update the parameters ?