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Exploring Weight Agnostic Neural Networks

Tuesday, August 27, 2019

Posted by Adam Gaier, Student Researcher and David Ha, Staff Research Scientist, Google Research, Tokyoneural networkimage classificationreinforcement learningneural architecture searchconvolutional network componentstransformer blocksconvolutional networksinductive biasesrandomly initializednewprecocial speciesanti-predator behaviorsWeight Agnostic Neural Networkscode

Left: A hand-engineered, fully-connected deep neural network with 2760 weight connections. Using a learning algorithm, we can solve for the set of 2760 weight parameters so that this network can perform the BipedalWalker-v2 task. Right: A weight agnostic neural network architecture with 44 connections that can perform the same Bipedal Walker task. Unlike the fully-connected network, this WANN can still perform the task without the need to train the weight parameters of each connection. In fact, to simplify the training, the WANN is designed to perform when the values of each weight connection are identical, or shared, and it will even function if this shared weight parameter is randomly sampled.

Finding WANNstopologysearch algorithm

Operators for searching the space of network topologies
Left: A minimal network topology, with input and outputs only partially connected.
Middle: Networks are altered in one of three ways:
(1) Insert Node: a new node is inserted by splitting an existing connection.
(2) Add Connection: a new connection is added by connecting two previously unconnected nodes.
(3) Change Activation: the activation function of a hidden node is reassigned.
Right: Possible activation functions (linear, step, sin, cosine, Gaussian, tanh, sigmoid, inverse, absolute value, ReLU)

multi-objective optimization

Overview of Weight Agnostic Neural Network Search and corresponding operators for searching the space of network topologies.

Training WANN Architecturesto some extentswing-up cartpole task

A WANN performing a Cartpole Swing-up task at various different weight parameters, and also using fine-tuned weight parameters.

starting pointhow animals learn

Through the use of multi-objective optimization for both performance and network simplicity, our method found a simple WANN for a Car Racing from pixels task that works well without explicitly training for the weights of the network.