A Policy-switching learning approach for adaptive spoken dialogue agents

The reinforcement learning paradigm has been adopted for inferring optimized and adaptive spoken dialogue agents. Such agents are typically learnt and tested without combining competing agents that may yield better performance at some points in the conversation. This paper presents an approach that learns dialogue behaviour from competing agents—switching from one policy to another competing one—on a previously proposed hierarchical learning framework. This policy-switching approach was investigated using a simulated flight booking dialogue system based on different types of information request. Experimental results reported that the induced agent using the proposed policy-switching approach yielded 8.2% fewer system actions than three baselines with a fixed type of information request. This result suggests that the proposed approach is useful for learning adaptive and scalable spoken dialogue agents.