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arXiv · 1710.11277

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

Baolin Peng·Xiujun Li·Jianfeng Gao·Jingjing Liu·Yun-Nung Chen·Kam-Fai Wong
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Abstract

This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts. Then, we incorporate the discriminator as another critic into the advantage actor-critic (A2C) framework, to encourage the dialogue agent to explore state-action within the regions where the agent takes actions similar to those of the experts. Experimental results in a movie-ticket booking domain show that the proposed Adversarial A2C can accelerate policy exploration efficiently.

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Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. 2017-10-31. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. https://arxiv.org/abs/1710.11277

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