Read the following two resources to realize the inherent complexity in GANs and tuning them. No need to get into intimate detail, just skim at this point. This information will come in handy when you're looking at papers to see which techniques they've implemented.
What I've found works best is if you checkpoint your model when you're starting to get some "knowable" results (ie. your GAN is showing promise). From there, you spin up separate experiments and run each for a number of epochs.
Something like this is good: