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