2 research outputs found
Modularity as a Means for Complexity Management in Neural Networks Learning
Training a Neural Network (NN) with lots of parameters or intricate
architectures creates undesired phenomena that complicate the optimization
process. To address this issue we propose a first modular approach to NN
design, wherein the NN is decomposed into a control module and several
functional modules, implementing primitive operations. We illustrate the
modular concept by comparing performances between a monolithic and a modular NN
on a list sorting problem and show the benefits in terms of training speed,
training stability and maintainability. We also discuss some questions that
arise in modular NNs.Comment: Full-paper submited to the AAAI-MAKE 201
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
In this work, we propose a novel memory-based multi-agent meta-learning
architecture and learning procedure that allows for learning of a shared
communication policy that enables the emergence of rapid adaptation to new and
unseen environments by learning to learn learning algorithms through
communication. Behavior, adaptation and learning to adapt emerges from the
interactions of homogeneous experts inside a single agent. The proposed
architecture should allow for generalization beyond the level seen in existing
methods, in part due to the use of a single policy shared by all experts within
the agent as well as the inherent modularity of 'Badger'