[Question] why SAC train method updates the target every steps?

### ❓ Question

Hi, I was looking into the code inside the SAC algorithm, and I encountered an odd [line here](https://github.com/DLR-RM/stable-baselines3/blob/2dce430161087c46b9e668f56f3c94c634eebff8/stable_baselines3/sac/sac.py#L290).

When comparing to make the update of the target, should not be using self._n_updates, or something similar instead gradient_step? such variable is always zero, hence, is always entering in the condition?.

There is maybe any specific reason to do it this way?

Thanks!

### Checklist

- [x] I have checked that there is no similar [issue](https://github.com/DLR-RM/stable-baselines3/issues) in the repo
- [x] I have read the [documentation](https://stable-baselines3.readthedocs.io/en/master/)
- [ ] If code there is, it is [minimal and working](https://github.com/DLR-RM/stable-baselines3/issues/982#issuecomment-1197044014)
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