  Interactive Fiction (IF) games with real human-written natural language texts
provide a new natural evaluation for language understanding techniques. In
contrast to previous text games with mostly synthetic texts, IF games pose
language understanding challenges on the human-written textual descriptions of
diverse and sophisticated game worlds and language generation challenges on the
action command generation from less restricted combinatorial space. We take a
novel perspective of IF game solving and re-formulate it as Multi-Passage
Reading Comprehension (MPRC) tasks. Our approaches utilize the context-query
attention mechanisms and the structured prediction in MPRC to efficiently
generate and evaluate action outputs and apply an object-centric historical
observation retrieval strategy to mitigate the partial observability of the
textual observations. Extensive experiments on the recent IF benchmark
(Jericho) demonstrate clear advantages of our approaches achieving high winning
rates and low data requirements compared to all previous approaches. Our source
code is available at: https://github.com/XiaoxiaoGuo/rcdqn.
