How to make virtual organisms learn using neural networks? [closed]

As mentioned by Mika Fischer, this sounds similar to artificial life problems, so that's one avenue you could look at.

It also sounds a bit like you're trying to reinvent Reinforcement Learning. I would recommend reading through Reinforcement Learning: An Introduction, which is freely available in HTML form at that website, or purchasable in dead tree format. Example code and solutions are also provided on that page.

Use of neural networks (and other function approximators) and planning techniques is discussed later in the book, so don't get discouraged if the initial stuff seems too basic or non-applicable to your problem.

This is similar to issues with trying to find a global minimum, where it's easy to get stuck in a local minimum. Consider trying to find the global minimum for the profile below: you place the ball in different places and follow it as it rolls down the hill to the minimum, but depending on where you place it, you may get stuck in a local dip.

That is, in complicated situations, you can't always get to the best solution from all starting points using small optimizing increments. The general solutions to this are to fluctuate the parameters (i.e., weights, in this case) more vigorously (and usually reduce the size of the fluctuations as you progress the simulation -- like in simulated annealing), or just realize that a bunch of the starting points aren't going to go anywhere interesting.

How do you want it to learn? You don't like the fact that randomly seeded organisms either die off or prosper, but the only time you provide feedback into your organism is if they randomly get food.

Let's model this as hot and cold. Currently, everything feeds back "cold" except when the organism is right on top of food. So the only opportunity to learn is accidentally running over food. You can tighten this loop to provide more continuous feedback if you desire. Feedback warmer if there is movement toward food, cold if moving away.

Now, the downside of this is that there is no input for anything else. You've only got a food-seeker learning technique. If you want your organisms to find a balance between hunger and something else (say, overcrowding avoidance, mating, etc), the whole mechanism probably needs to be re-thought.