  Dialogue policy plays an important role in task-oriented spoken dialogue
systems. It determines how to respond to users. The recently proposed deep
reinforcement learning (DRL) approaches have been used for policy optimization.
However, these deep models are still challenging for two reasons: 1) Many
DRL-based policies are not sample-efficient. 2) Most models don't have the
capability of policy transfer between different domains. In this paper, we
propose a universal framework, AgentGraph, to tackle these two problems. The
proposed AgentGraph is the combination of GNN-based architecture and DRL-based
algorithm. It can be regarded as one of the multi-agent reinforcement learning
approaches. Each agent corresponds to a node in a graph, which is defined
according to the dialogue domain ontology. When making a decision, each agent
can communicate with its neighbors on the graph. Under AgentGraph framework, we
further propose Dual GNN-based dialogue policy, which implicitly decomposes the
decision in each turn into a high-level global decision and a low-level local
decision. Experiments show that AgentGraph models significantly outperform
traditional reinforcement learning approaches on most of the 18 tasks of the
PyDial benchmark. Moreover, when transferred from the source task to a target
task, these models not only have acceptable initial performance but also
converge much faster on the target task.
