Models, code, and papers for "Jacob Carse":

Deep Reinforcement Learning for Multi-Domain Dialogue Systems

Nov 26, 2016
Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, Jacob Carse

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.

* NIPS Workshop on Deep Reinforcement Learning, 2016 

  Click for Model/Code and Paper