Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems. However, extending RL-based systems to handle new intents and slots requires a system redesign. The high maintenance cost makes it difficult to apply RL methods to practical systems on a large scale. To address this issue, we propose a practical teacherstudent framework to extend RL-based dialog systems without retraining from scratch. Specifically, the "student" is an extended dialog manager based on a new

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... ntology, and the "teacher" is existing resources used for guiding the learning process of the "student". By specifying constraints held in the new dialog manager, we transfer knowledge of the "teacher" to the "student" without additional resources. Experiments show that the performance of the extended system is comparable to the system trained from scratch. More importantly, the proposed framework makes no assumption about the unsupported intents and slots, which makes it possible to improve RL-based systems incrementally.