If you add both losses, gradient magnitude and direction for weights do not change when compared to doing backwards twice for both loss values. Mathematically resulting gradients will be the same in both cases thus taking derivatives once is more efficient.
From another point of view is that your network can not optimize two loss functions separately, in your case you only have one loss function with two sub loss functions which is policy_losses+value_losses.
Hope this helps.
Best.