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arXiv · 1511.04636

Deep Reinforcement Learning with a Natural Language Action Space

Ji He·Jianshu Chen·Xiaodong He·Jianfeng Gao·Lihong Li·Li Deng·Mari Ostendorf
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Abstract

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games. Termed a deep reinforcement relevance network (DRRN), the architecture represents action and state spaces with separate embedding vectors, which are combined with an interaction function to approximate the Q-function in reinforcement learning. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.

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Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf. 2016-06-08. Deep Reinforcement Learning with a Natural Language Action Space. https://arxiv.org/abs/1511.04636

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