Model-based Bayesian Reinforcement Learning for Dialogue Management
This work addresses dialogue management for human-robot interaction, presenting an incremental improvement by incorporating Bayesian methods and domain knowledge into reinforcement learning.
The paper tackles the problem of optimizing dialogue policies by proposing a model-based Bayesian reinforcement learning approach, which uses Bayesian inference to refine a posterior distribution over model parameters and plan actions, demonstrating benefits through empirical results on a user simulator from human-robot interaction data.
Reinforcement learning methods are increasingly used to optimise dialogue policies from experience. Most current techniques are model-free: they directly estimate the utility of various actions, without explicit model of the interaction dynamics. In this paper, we investigate an alternative strategy grounded in model-based Bayesian reinforcement learning. Bayesian inference is used to maintain a posterior distribution over the model parameters, reflecting the model uncertainty. This parameter distribution is gradually refined as more data is collected and simultaneously used to plan the agent's actions. Within this learning framework, we carried out experiments with two alternative formalisations of the transition model, one encoded with standard multinomial distributions, and one structured with probabilistic rules. We demonstrate the potential of our approach with empirical results on a user simulator constructed from Wizard-of-Oz data in a human-robot interaction scenario. The results illustrate in particular the benefits of capturing prior domain knowledge with high-level rules.