Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

EMNLP 2018 Shang-Yu SuXiujun LiJianfeng GaoJingjing LiuYun-Nung Chen

This paper presents a Discriminative Deep Dyna-Q (D3Q) approach to improving the effectiveness and robustness of Deep Dyna-Q (DDQ), a recently proposed framework that extends the Dyna-Q algorithm to integrate planning for task-completion dialogue policy learning. To obviate DDQ's high dependency on the quality of simulated experiences, we incorporate an RNN-based discriminator in D3Q to differentiate simulated experience from real user experience in order to control the quality of training data... (read more)

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