Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

@inproceedings{Peng2017CompositeTD,
  title={Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning},
  author={Baolin Peng and Xiujun Li and L. Li and Jianfeng Gao and A. Çelikyilmaz and Sungjin Lee and K. Wong},
  booktitle={EMNLP},
  year={2017}
}
  • Baolin Peng, Xiujun Li, +4 authors K. Wong
  • Published in EMNLP 2017
  • Computer Science
  • Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep… CONTINUE READING
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