PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel,
For artificial general intelligence (AGI) it would be efficient if multiple users
trained the same giant neural network, permitting parameter reuse, without
catastrophic forgetting. PathNet is a first step in this direction. It is a neural
network algorithm that uses agents embedded in the neural network whose task is to
discover which parts of the network to re-use for new tasks. Agents are pathways
(views) through the network which determine the subset of parameters that are used
and updated by the forwards and backwards passes of the backpropogation algorithm.
During learning, a tournament selection genetic algorithm is used to select
pathways through the neural network for replication and mutation. Pathway fitness
is the performance of that pathway measured according to a cost function. We
demonstrate successful transfer learning; fixing the parameters along a path
learned on task A and re-evolving a new population of paths for task B, allows task
B to be learned faster than it could be learned from scratch or after fine-tuning.
Paths evolved on task B re-use parts of the optimal path evolved on task A.
Positive transfer was demonstrated for binary MNIST, CIFAR, and SVHN supervised
learning classification tasks, and a set of Atari and Labyrinth reinforcement
learning tasks, suggesting PathNets have general applicability for neural network
training. Finally, PathNet also significantly improves the robustness to
hyperparameter choices of a parallel asynchronous reinforcement learning algorithm