  The wiring of neurons in the brain is more flexible than the wiring of
connections in contemporary artificial neural networks. It is possible that
this extra flexibility is important for efficient problem solving and learning.
  This paper introduces the Interaction Network. Interaction Networks aim to
capture some of this extra flexibility.
  An Interaction Network consists of a collection of conventional neural
networks, a set of memory locations, and a DQN or other reinforcement learner.
The DQN decides when each of the neural networks is executed, and on what
memory locations. In this way, the individual neural networks can be trained on
different data, for different tasks. At the same time, the results of the
individual networks influence the decision process of the reinforcement
learner. This results in a feedback loop that allows the DQN to perform actions
that improve its own decision-making.
  Any existing type of neural network can be reproduced in an Interaction
Network in its entirety, with only a constant computational overhead.
  Interaction Networks can then introduce additional features to improve
performance further. These make the algorithm more flexible and general, but at
the expense of being harder to train.
  In this paper, thought experiments are used to explore how the additional
abilities of Interaction Networks could be used to improve various existing
types of neural networks.
  Several experiments have been run to prove that the concept is sound. These
show that the basic idea works, but they also reveal a number of challenges
that do not appear in conventional neural networks, which make Interaction
Networks very hard to train.
  Further research needs to be done to alleviate these issues. A number of
promising avenues of research to achieve this are outlined in this paper.
