Pre-train using sigmoid and train using ReLU?
Since RBM has only one layer of weights, why you bother changing sigmoid to ReLU in 1-layer net? Gradient vanishing is very unlikely to happen in such a shallow net.

You can also train Gaussian-Bernoulli or Gaussian-Gaussian RBM (more here), which has identity activation function, which is closer to ReLU than sigmoid, and what is more justified if you have real-valued data, not binary. However, these types of networks are a bit less stable to train because of this unconstrained activation.