  This paper presents an end-to-end framework for task-oriented dialog systems
using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to
interface with a relational database and jointly learn policies for both
language understanding and dialog strategy. Moreover, we propose a hybrid
algorithm that combines the strength of reinforcement learning and supervised
learning to achieve faster learning speed. We evaluated the proposed model on a
20 Question Game conversational game simulator. Results show that the proposed
method outperforms the modular-based baseline and learns a distributed
representation of the latent dialog state.
