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This paper presents an end-to-end framework for task-oriented dialog systemsusing a variant of Deep Recurrent Q-Networks (DRQN). The model is able tointerface with a relational database and jointly learn policies for bothlanguage understanding and dialog strategy. Moreover, we propose a hybridalgorithm that combines the strength of reinforcement learning and supervisedlearning to achieve faster learning speed. We evaluated the proposed model on a20 Question Game conversational game simulator. Results show that the proposedmethod outperforms the modular-based baseline and learns a distributedrepresentation of the latent dialog state.

Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning