Weight Agnostic Neural Networks

Not all neural network architectures are created equal, some perform much better than others for certain tasks. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. In contrast, when we train artificial agents to perform a task, we typically choose a neural network architecture we believe to be suitable for encoding a policy for the task, and find the weight parameters of this policy using a learning algorithm.

Source: weightagnostic.github.io