How to build in symmetry of inputs into a Deep Neural Network?
This might not be what you're looking for, but you can use data augmentation to do this: from each sample, you can generate multiple samples by cloning it, generating a random permutation and permuting the input and the expected output (for an autoencoder, this should achieve the same goal). This doesn't guarantee complete permutation invariance but if you do it enough times for each sample it should get you pretty close.