Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning



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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.

This paper has been published at EMNLP 2020

Please cite our work using the BibTeX below.

      title={Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning}, 
      author={Xiaoxiao Guo and Mo Yu and Yupeng Gao and Chuang Gan and Murray Campbell and Shiyu Chang},
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