Expert-based reward shaping and exploration scheme for boosting policy learning of dialogue management

This paper investigates the conditions under which expert knowledge can be used to accelerate the policy optimization of a learning agent. Recent works on reinforcement learning for dialogue management allowed to devise sophisticated methods for value estimation in order to deal all together with exploration/exploitation dilemma, sample-efficiency and non-stationary environments. In this paper, a reward shaping method and an exploration scheme, both based on some intuitive hand-coded expert advices, are combined with an efficient temporal difference-based learning procedure. The key objective is to boost the initial training stage, when the system is not sufficiently reliable to interact with real users (e.g. clients). Our claims are illustrated by experiments based on simulation and carried out using a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS).