In some ways it is, but the main difference is that adversarial learning (usually) produces a second neural network whose purpose is to exploit weakness is the first. Whereas reinforcement learning does not produce a second neural network to beat the first, it uses what it learned to solely improve the original.
As a side note, the main application I have seen with adversarial learning research is with photo recognition, but I guess you could have an adversarial network exist to help help improve an object recognition network. At that point it would probably become something between adversarial and reinforcement learning. However, with game based reinforcement learning, it doesn't require a second specific network as the adversary, it can easily just be paired against itself.
It isn't a dumb question, they are very similar in some ways. They mainly differ in what exactly the goal of the opponent is. In this case, it is to help improve itself, however in typical adversarial situations it is solely to exploit (become its adversary).