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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 . 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 – 63 citations

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Agentic Dialogue & Multi Turn Efficiency ICASSP Reinforcement Learning Security Tools

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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