I am wondering how to go about training a neural network without providing it with training values. My premise for this is that the neural network(s) will be used on a robot that can receive positive/negative feedback from sensors. IE, in order to train it to roam freely without bumping into things, a positive feedback occurs when no collision sensors or proximity sensors are triggered. A negative feedback occurs when the collision/proximity sensors ARE triggered. How can the neural network be trained using this method?
I am writing this in C++
What you describe is called reinforcement learning. It could be applied to neural networks, but does not require them in general. The canonical textbook to read on the subject is Reinforcement Learning: An Introduction by Richard Sutton and Andrew Barto. The connection between neural networks and reinforcement learning is explored in a bit more detail in the PDP Handbook by James McClelland.