  One of the key challenges of artificial intelligence is to learn models that
are effective in the context of planning. In this document we introduce the
predictron architecture. The predictron consists of a fully abstract model,
represented by a Markov reward process, that can be rolled forward multiple
"imagined" planning steps. Each forward pass of the predictron accumulates
internal rewards and values over multiple planning depths. The predictron is
trained end-to-end so as to make these accumulated values accurately
approximate the true value function. We applied the predictron to procedurally
generated random mazes and a simulator for the game of pool. The predictron
yielded significantly more accurate predictions than conventional deep neural
network architectures.
