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      Language Understanding for Text-based Games Using Deep Reinforcement Learning

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          Abstract

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

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          Glove Global Vectors for Word Representation

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            Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

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              Prioritized sweeping: Reinforcement learning with less data and less time

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                Author and article information

                Journal
                2015-06-30
                2015-09-11
                Article
                1506.08941

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                11 pages, Appearing at EMNLP, 2015
                cs.CL cs.AI

                Theoretical computer science, Artificial intelligence

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