  Dialog policy determines the next-step actions for agents and hence is
central to a dialogue system. However, when migrated to novel domains with
little data, a policy model can fail to adapt due to insufficient interactions
with the new environment. We propose Deep Transferable Q-Network (DTQN) to
utilize shareable low-level signals between domains, such as dialogue acts and
slots. We decompose the state and action representation space into feature
subspaces corresponding to these low-level components to facilitate
cross-domain knowledge transfer. Furthermore, we embed DTQN in a meta-learning
framework and introduce Meta-DTQN with a dual-replay mechanism to enable
effective off-policy training and adaptation. In experiments, our model
outperforms baseline models in terms of both success rate and dialogue
efficiency on the multi-domain dialogue dataset MultiWOZ 2.0.
