@inproceedings{he2016deep,
author = {He, Ji and Chen, Jianshu and He, Xiaodong and Gao, Jianfeng and Li, Lihong and Deng, Li and Ostendorf, Mari},
title = {Deep Reinforcement Learning with a Natural Language Action Space},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2016},
month = {August},
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.},
publisher = {ACL - Association for Computational Linguistics},
url = {http://approjects.co.za/?big=en-us/research/publication/deep-reinforcement-learning-natural-language-action-space/},
edition = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL)},
}