  This paper introduces a novel architecture for reinforcement learning with
deep neural networks designed to handle state and action spaces characterized
by natural language, as found in text-based games. Termed a deep reinforcement
relevance network (DRRN), the architecture represents action and state spaces
with separate embedding vectors, which are combined with an interaction
function to approximate the Q-function in reinforcement learning. We evaluate
the DRRN on two popular text games, showing superior performance over other
deep Q-learning architectures. Experiments with paraphrased action descriptions
show that the model is extracting meaning rather than simply memorizing strings
of text.
