  Text-based games(TBG) are complex environments which allow users or computer
agents to make textual interactions and achieve game goals.In TBG agent design
and training process, balancing the efficiency and performance of the agent
models is a major challenge. Finding TBG agent deep learning modules'
performance in standardized environments, and testing their performance among
different evaluation types is also important for TBG agent research. We
constructed a standardized TBG agent with no hand-crafted rules, formally
categorized TBG evaluation types, and analyzed selected methods in our
environment.
