Distributed Structured Actor-Critic Reinforcement Learning for Universal Dialogue Management
By Zhi Chen, Lu Chen, Xiaoyuan Liu, Kai Yu. 2020
22 citations recorded by the source corpus.
Traditional dialogue policy needs to be trained independently for each dialogue task. In this work, we aim to solve a collection of independent dialogue tasks using a unified dialogue agent. The unified policy is parallelly trained using the conversation data from all of the distributed dialogue tasks. However, there are two key challenges:(1) the design of a unified dialogue model to adapt to different dialogue tasks; (2) finding a robust reinforcement learning method to keep the efficiency and the stability of the training process. Here we propose a novel structured actor-critic approach to implement structured deep reinforcement learning (DRL), which not only can learn parallelly from data of different dialogue tasks but also achieves stable and sample-efficient learning. We demonstrate the effectiveness of the proposed approach on 18 tasks of PyDial benchmark. The results show that our method is able to achieve state-of-the-art performance.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Stefan Wermter
- Cornelius Weber
- Michael Heck
- Carel van Niekerk
- Nicolás Navarro-Guerrero
- Anabel Martín-González
- Nurul Lubis
- Milica Gavsi'c
Citations in the corpus
- Dynamic Dialogue Policy for Continual Reinforcement Learning (2022, methodology): “In the realm of domain adaptation, works assume a fixed number of actions that are slot-independent, and focus on the inclusion of slot-dependent actions when the domain changes (Wang et al., 2015; Casanueva et al., 2018; Chen et al., 2018; Chen et al., 2020; Lin et al., 2021).”
- Dynamic Dialogue Policy Transformer for Continual Reinforcement Learning (2022, methodology): “In the realm of domain adaptation, works assume a fixed number of actions that are slot-independent, and focus on the inclusion of slot-dependent actions when the domain changes (Wang et al., 2015; Casanueva et al., 2018; Chen et al., 2018; Chen et al., 2020; Lin et al., 2021).”
- Survey on reinforcement learning for language processing (2021, methodology): “In [14], a structured actor-critic model is proposed to implement structured deep reinforcement learning.”