  We introduce a generalization of temporal-difference (TD) learning to
networks of interrelated predictions. Rather than relating a single prediction
to itself at a later time, as in conventional TD methods, a TD network relates
each prediction in a set of predictions to other predictions in the set at a
later time. TD networks can represent and apply TD learning to a much wider
class of predictions than has previously been possible. Using a random-walk
example, we show that these networks can be used to learn to predict by a fixed
interval, which is not possible with conventional TD methods. Secondly, we show
that if the inter-predictive relationships are made conditional on action, then
the usual learning-efficiency advantage of TD methods over Monte Carlo
(supervised learning) methods becomes particularly pronounced. Thirdly, we
demonstrate that TD networks can learn predictive state representations that
enable exact solution of a non-Markov problem. A very broad range of
inter-predictive temporal relationships can be expressed in these networks.
Overall we argue that TD networks represent a substantial extension of the
abilities of TD methods and bring us closer to the goal of representing world
knowledge in entirely predictive, grounded terms.
