  Recently, text world games have been proposed to enable artificial agents to
understand and reason about real-world scenarios. These text-based games are
challenging for artificial agents, as it requires an understanding of and
interaction using natural language in a partially observable environment.
Agents observe the environment via textual descriptions designed to be
challenging enough for even human players. Past approaches have not paid enough
attention to the language understanding capability of the proposed agents.
Typically, these approaches train from scratch, an agent that learns both
textual representations and the gameplay online during training using a
temporal loss function. Given the sample-inefficiency of RL approaches, it is
inefficient to learn rich enough textual representations to be able to
understand and reason using the textual observation in such a complicated game
environment setting. In this paper, we improve the semantic understanding of
the agent by proposing a simple RL with LM framework where we use
transformer-based language models with Deep RL models. We perform a detailed
study of our framework to demonstrate how our model outperforms all existing
agents on the popular game, Zork1, to achieve a score of 44.7, which is 1.6
higher than the state-of-the-art model. Overall, our proposed approach
outperforms 4 games out of the 14 text-based games, while performing comparable
to the state-of-the-art models on the remaining games.
