 
Summary: Reinforcement Learning with Modular Neural Networks for Control
Charles W. Anderson Zhaohui Hong
Department of Computer Science
Colorado State University
Fort Collins, CO 80523
anderson@cs.colostate.edu
Abstract
Reinforcement learning methods can be applied to
control problems with the objective of optimizing the
value of a function over time. They have been used
to train single neural networks that learn solutions to
whole tasks. Jacobs and Jordan 5 have shown that a
set of expert networks combined via a gating network
can more quickly learn tasks that can be decomposed.
Even the decomposition can be learned. Inspired by
Boyan's work of modular neural networks for learning
with temporaldi erence methods 4 , we modify the
reinforcement learning algorithm called QLearning to
train a modular neural network to solve a control prob
lem. The resulting algorithm is demonstrated on the
