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Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu · Meng Fang · Ling Chen · Yali Du · Joey Tianyi Zhou · Chengqi Zhang

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #573

We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.

Author Information

Yunqiu Xu (University of Technology Sydney)
Meng Fang (Tencent)
Ling Chen (" University of Technology, Sydney, Australia")
Yali Du (University College London)

I am currently a research fellow at UCL. I am interested in multi-agent reinforcement learning, adversarial machine learning and recommendation systems.

Joey Tianyi Zhou (IHPC, A*STAR)
Chengqi Zhang (University of Technology Sydney)

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