  Human ability at solving complex tasks is helped by priors on object and
event semantics of their environment. This paper investigates the use of
similar prior knowledge for transfer learning in Reinforcement Learning agents.
In particular, the paper proposes to use a first-order-logic language grounded
in deep neural networks to represent facts about objects and their semantics in
the real world. Facts are provided as background knowledge a priori to learning
a policy for how to act in the world. The priors are injected with the
conventional input in a single agent architecture. As proof-of-concept, the
paper tests the system in simple experiments that show the importance of
symbolic abstraction and flexible fact derivation. The paper shows that the
proposed system can learn to take advantage of both the symbolic layer and the
image layer in a single decision selection module.
