  Recent works usually address Dialog policy learning DPL by training a
reinforcement learning (RL) agent to determine the best dialog action. However,
existing works on deep RL require a large volume of agent-user interactions to
achieve acceptable performance. In this paper, we propose to make full use of
the plain text knowledge from the pre-trained language model to accelerate the
RL agent's learning speed. Specifically, we design a dialog action-aware
transformer encoder (DaTrans), which integrates a new fine-tuning procedure
named masked last action task to encourage DaTrans to be dialog-aware and
distils action-specific features. Then, DaTrans is further optimized in an RL
setting with ongoing interactions and evolves through exploration in the dialog
action space toward maximizing long-term accumulated rewards. The effectiveness
and efficiency of the proposed model are demonstrated with both simulator
evaluation and human evaluation.
