Using Wizard-of-Oz simulations to bootstrap Reinforcement - Learning based dialog management systems

This paper describes a method for “bootstrapping” a Reinforcement Learningbased dialog manager using a Wizard-ofOz trial. The state space and action set are discovered through the annotation, and an initial policy is generated using a Supervised Learning algorithm. The method is tested and shown to create an initial policy which performs significantly better and with less effort than a handcrafted policy, which can be generated using a small number of dialogs.