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

Understand

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.