Language Understanding for Text-based Games Using Deep Reinforcement\n Learning

2015/06/30 by Karthik Narasimhan, Tejas Kulkarni, Narasimhan, Karthik +3 · 2 voices · 51 citations
Computer Science · #Action (physics) #Artificial Intelligence in Games #Artificial intelligence #Bigram #Computer science #Multimedia #Natural language processing #Programming language #Reinforcement Learning in Robotics #Reinforcement learning #Semantics (computer science) #State (computer science) #Task (project management) #Topic Modeling #Video game #cs.AI #cs.CL

paper · pdf · doi:10.48550/arxiv.1506.08941

published in arXiv (Cornell University) (Cornell University) · 11 pages, Appearing at EMNLP, 2015

openalex publication_date 2015/06/30 · arxiv created 2015/09/11 · arxiv updated 2015/09/15 · openalex created_date 2022/10/05 · openalex updated_date 2026/08/05

Abstract

In this paper, we consider the task of learning control policies for\ntext-based games. In these games, all interactions in the virtual world are\nthrough text and the underlying state is not observed. The resulting language\nbarrier makes such environments challenging for automatic game players. We\nemploy a deep reinforcement learning framework to jointly learn state\nrepresentations and action policies using game rewards as feedback. This\nframework enables us to map text descriptions into vector representations that\ncapture the semantics of the game states. We evaluate our approach on two game\nworlds, comparing against baselines using bag-of-words and bag-of-bigrams for\nstate representations. Our algorithm outperforms the baselines on both worlds\ndemonstrating the importance of learning expressive representations.\n

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Discussions

• Language Understanding for Text-based Games Using Deep Reinforcement Learning [pdf] [hn, 31 points, 6 comments]
• MIT AI bot learns aspects of language by autonomously playing text-games [hn, 2 points, 0 comments]

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