  Text-based games simulate worlds and interact with players using natural
language. Recent work has used them as a testbed for autonomous
language-understanding agents, with the motivation being that understanding the
meanings of words or semantics is a key component of how humans understand,
reason, and act in these worlds. However, it remains unclear to what extent
artificial agents utilize semantic understanding of the text. To this end, we
perform experiments to systematically reduce the amount of semantic information
available to a learning agent. Surprisingly, we find that an agent is capable
of achieving high scores even in the complete absence of language semantics,
indicating that the currently popular experimental setup and models may be
poorly designed to understand and leverage game texts. To remedy this
deficiency, we propose an inverse dynamics decoder to regularize the
representation space and encourage exploration, which shows improved
performance on several games including Zork I. We discuss the implications of
our findings for designing future agents with stronger semantic understanding.
