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 Qlearning architectures. Experiments with paraphrased action descriptions show that the model is extracting meaning rather than simply memorizing strings of text.
REF present the Deep Reinforcement Relevance Network (DRRN) which uses two separate deep neural networks to encode the state and actions.
Deep Reinforcement Learning with a Natural Language Action Space
ACL 2016
arXiv: Artificial Intelligence
Computer Science