<p>Are you thinking of using asynchronous methods for GAN training similar to how asynchronous updates used in A3C? </p>

<p>I guess the motivation for asynchronous methods in RL is quite different from what you want to solve with async methods in GANs.</p>

<p>RL can be unstable (without async methods) because of the non-stationary nature of the data (i.e. high correlation between consecutive updates). And solving this using async methods kind of makes sense.</p>

<p>GANs are unstable because of the optimization approach (e.g. mini-max) the take in solving the objective function. And more recent GAN variants (e.g. progressive GANs) are significant improvements of the original GAN. Personally, I believe "mode collapse" to be a more pressing matter than the stability. </p>

<p>So not sure if async methods is the answer you're looking for solving stability issues in GANs. Maybe better optimization methods (e.g. penalizing in stabilities in the optimization procedure) might be a better way of going about this?</p>

<p>References
<a href="https://arxiv.org/pdf/1705.07215.pdf" rel="nofollow noreferrer">On Convergence and Stability of GANs</a></p>
Any Asynchronous Training Methods for GAN?
<p>GAN sometimes get really unstable with the high dimensional data. Can we train GAN is Asynchronous manner? It's like we have one master Generator and Discriminator. But we actually update it asynchronously with gradients from a number of slaves generators and discriminators.</p>