Deep Reinforcement Learning with a Natural Language Action Space

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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.