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

TL;DR

This paper explores using recurrent neural networks for reward shaping in spoken dialogue systems to accelerate online policy learning, demonstrating improved learning speed without prior user goal knowledge.

Contribution

It introduces three RNN-based reward shaping methods that enhance policy learning speed in dialogue systems without needing prior user goal information.

Findings

RNN reward shaping improves learning speed in simulated dialogues

RNNs effectively guide real user interactions without prior goal knowledge

Methods outperform baseline in both simulated and real scenarios

Abstract

Statistical spoken dialogue systems have the attractive property of being able to be optimised from data via interactions with real users. However in the reinforcement 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 learning costs are expensive. Reward shaping is one promising technique for addressing these concerns. Here we examine three recurrent 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 generated by a simulated user and attempt to diffuse the overall evaluation of the dialogue 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.