  The task-oriented spoken dialogue system (SDS) aims to assist a human user in
accomplishing a specific task (e.g., hotel booking). The dialogue management is
a core part of SDS. There are two main missions in dialogue management:
dialogue belief state tracking (summarising conversation history) and dialogue
decision-making (deciding how to reply to the user). In this work, we only
focus on devising a policy that chooses which dialogue action to respond to the
user. The sequential system decision-making process can be abstracted into a
partially observable Markov decision process (POMDP). Under this framework,
reinforcement learning approaches can be used for automated policy
optimization. In the past few years, there are many deep reinforcement learning
(DRL) algorithms, which use neural networks (NN) as function approximators,
investigated for dialogue policy.
