  Deep reinforcement learning (DRL) brings the power of deep neural networks to
bear on the generic task of trial-and-error learning, and its effectiveness has
been convincingly demonstrated on tasks such as Atari video games and the game
of Go. However, contemporary DRL systems inherit a number of shortcomings from
the current generation of deep learning techniques. For example, they require
very large datasets to work effectively, entailing that they are slow to learn
even when such datasets are available. Moreover, they lack the ability to
reason on an abstract level, which makes it difficult to implement high-level
cognitive functions such as transfer learning, analogical reasoning, and
hypothesis-based reasoning. Finally, their operation is largely opaque to
humans, rendering them unsuitable for domains in which verifiability is
important. In this paper, we propose an end-to-end reinforcement learning
architecture comprising a neural back end and a symbolic front end with the
potential to overcome each of these shortcomings. As proof-of-concept, we
present a preliminary implementation of the architecture and apply it to
several variants of a simple video game. We show that the resulting system --
though just a prototype -- learns effectively, and, by acquiring a set of
symbolic rules that are easily comprehensible to humans, dramatically
outperforms a conventional, fully neural DRL system on a stochastic variant of
the game.
