Has anyone studied and/or implemented a network training system such as described in "The Truck Backer-Upper: An Example of Self-Learning in Neural Networks" by Nguyen and Widrow? Here is a link to their article:
This type of problem is interesting because it must emulate and evaluate the steering control effect on the truck with it positioned in different locations and orientations. The same approach should be useful for a wide variety of applications such as training a neural network as an autopilot.
After reading their article twice, I thought I understood how to do it. But when I began to think seriously about implementing it, the fog rolled in.
This is quite a different problem than training a network on a fixed set of data. It's an interesting hybrid between evolutionary programming and neural networks: The end product is a neural network, but the training involves evolutionary methods.
If anyone is interested in this type of problem, I would like to discuss it.