[Paper Review] Model-based Bayesian Reinforcement Learning for Dialogue Management

Pierre Lison|arXiv (Cornell University)|Apr 5, 2013

Speech and dialogue systems31 references4 citations

TL;DR

This paper proposes a model-based Bayesian reinforcement learning approach for dialogue management that maintains a posterior distribution over transition model parameters to handle uncertainty. By using probabilistic rules instead of unstructured multinomial distributions, the method achieves faster convergence and better generalization in a human-robot interaction scenario, as shown in experiments with Wizard-of-Oz data.

ABSTRACT

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.

Motivation & Objective

• To develop a model-based Bayesian reinforcement learning framework for dialogue policy learning that explicitly models uncertainty in transition dynamics.
• To investigate whether structured probabilistic rules outperform unstructured multinomial distributions in learning transition models.
• To evaluate the method in a real-world human-robot interaction scenario using a user simulator trained on Wizard-of-Oz data.
• To demonstrate that incorporating domain knowledge via high-level rules enhances learning efficiency and performance.

Proposed method

• The approach uses Bayesian inference to maintain a posterior distribution over model parameters, starting from a Dirichlet prior.
• The transition model is updated incrementally using observed state-action-observation triples from interactions.
• An approximate online planning algorithm selects actions based on the current belief state and the posterior over model parameters.
• Two formalizations are compared: one using standard multinomial distributions and another using structured probabilistic rules.
• The belief state is represented as a Bayesian network and updated using the POMDP belief update equation with observation likelihoods.
• The system uses a user simulator based on Wizard-of-Oz data to generate training interactions and evaluate performance.

Experimental results

Research questions

• RQ1 Can model-based Bayesian reinforcement learning effectively learn transition models in dialogue management with limited interaction data?
• RQ2 Does the use of structured probabilistic rules lead to faster convergence than unstructured multinomial distributions in learning transition dynamics?
• RQ3 How does model uncertainty, represented via a posterior over parameters, improve exploration and policy learning in partially observable environments?
• RQ4 To what extent can prior domain knowledge encoded in probabilistic rules enhance learning performance and generalization?
• RQ5 Can this approach achieve high performance without relying on pre-trained models or extensive simulation, enabling real-time online learning?

Key findings

• The probabilistic rules model converged to optimal Dirichlet parameters much faster than the multinomial model, indicating superior learning efficiency.
• The system using probabilistic rules achieved higher average return per episode compared to the multinomial model, demonstrating better policy performance.
• The K-L divergence between the estimated and actual user act distributions decreased more rapidly in the probabilistic rules model, indicating better model accuracy.
• The model-based approach successfully captured the underlying interaction dynamics, as evidenced by improved belief state estimation and action selection.
• The method showed potential for online learning from live interactions, as the rapid convergence of rule-based parameters suggests applicability in real-time deployment.