Standard deep reinforcement learning methods such as Deep Q-Networks (DQN)
for multiple tasks (domains) face scalability problems. We propose a method for
multi-domain dialogue policy learning---termed NDQN, and apply it to an
information-seeking spoken dialogue system in the domains of restaurants and
hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed)
using simulations report that our proposed method exhibits better scalability
and is promising for optimising the behaviour of multi-domain dialogue systems.