Reinforcement Learning of Question-Answering Dialogue Policies for Virtual Museum Guides

Abstract

We use Reinforcement Learning (RL) to learn question-answering dialogue policies for a real-world application. We analyze a corpus of interactions of museum visitors with two virtual characters that serve as guides at the Museum of Science in Boston, in order to build a realistic model of user behavior when interacting with these characters. A simulated user is built based on this model and used for learning the dialogue policy of the virtual characters using RL. Our learned policy out-performs two baselines (including the original dialogue policy that was used for collecting the corpus) in a simulation setting. 1