  Reinforcement learning algorithms such as Q-learning have shown great promise
in training models to learn the optimal action to take for a given system
state; a goal in applications with an exploratory or adversarial nature such as
task-oriented dialogues or games. However, models that do not have direct
access to their state are harder to train; when the only state access is via
the medium of language, this can be particularly pronounced. We introduce a new
model amenable to deep Q-learning that incorporates a Siamese neural network
architecture and a novel refactoring of the Q-value function in order to better
represent system state given its approximation over a language channel. We
evaluate the model in the context of zero-shot text-based adventure game
learning. Extrinsically, our model reaches the baseline's convergence
performance point needing only 15% of its iterations, reaches a convergence
performance point 15% higher than the baseline's, and is able to play unseen,
unrelated games with no fine-tuning. We probe our new model's representation
space to determine that intrinsically, this is due to the appropriate
clustering of different linguistic mediation into the same state.
