Reinforcement learning techniques for dialogue management

Abstract

Techniques are disclosed herein relating to using reinforcement learning to generate a dialogue policy. A computer system may perform an iterative training operation to train a deep Q-learning network (DQN) based on conversation logs from prior conversations. In various embodiments, the DQN may include an input layer to receive an input value indicative of a current state of a given conversation, one or more hidden layers, and an output layer that includes a set of nodes corresponding to available responses. During the iterative training operation, the disclosed techniques may analyze utterances from a conversation log and, based on the utterances, use the DQN to determine appropriate responses. Reward values may be determined based on the selected responses and, based on the reward values, the DQN may be updated. Once generated, the dialogue policy may be used by a chatbot system to guide conversations with users.