Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems

Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the rein-forcement learning paradigm the dialogue manager (agent) often requires significant time to explore the state-action space to learn to behave in a desirable manner. This is a critical issue when the system is trained on-line with real users where learn-ing costs are expensive. Reward shaping is one promising technique for addressing these concerns. Here we examine three re-current neural network (RNN) approaches for providing reward shaping information in addition to the primary (task-orientated) environmental feedback. These RNNs are trained on returns from dialogues gener-ated by a simulated user and attempt to diffuse the overall evaluation of the dia-logue back down to the turn level to guide the agent towards good behaviour faster. In both simulated and real user scenarios these RNNs are shown to increase policy learning speed. Importantly, they do not require prior knowledge of the user’s goal.