  In order to train a computer agent to play a text-based computer game, we
must represent each hidden state of the game. A Long Short-Term Memory (LSTM)
model running over observed texts is a common choice for state construction.
However, a normal Deep Q-learning Network (DQN) for such an agent requires
millions of steps of training or more to converge. As such, an LSTM-based DQN
can take tens of days to finish the training process. Though we can use a
Convolutional Neural Network (CNN) as a text-encoder to construct states much
faster than the LSTM, doing so without an understanding of the syntactic
context of the words being analyzed can slow convergence. In this paper, we use
a fast CNN to encode position- and syntax-oriented structures extracted from
observed texts as states. We additionally augment the reward signal in a
universal and practical manner. Together, we show that our improvements can not
only speed up the process by one order of magnitude but also learn a superior
agent.
