Lottery Ticket Theory says that some networks do not train well if at all, based on random properties of the network. In my current project, I have 100 agents, each with their own neural network, same shape. Each of these networks trains exactly the same way, but some end up acting smart and others dumb. This is a pool of just 100 networks.
In this experiment, the agents are trying to path to a given point on the terrain, outlined with a large yellow circle. As I move this circle, agents should change their direction toward the new position. What I do is I select a point, then enter the program into training mode, where all of the agents are training using exactly the same backpropagation ( but not genetics, ATM). Then, I turn off training and see how they behave.
As you can see, a small amount of the networks have failed to learn very well.