Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems
This work addresses the challenge of controlled self-learning for conversational AI systems, offering an incremental improvement to manage exploration risks in specific domains.
The paper tackles the problem of abrupt policy changes in conversational AI systems by introducing a scalable framework with user-defined constraints and a meta-gradient learning approach, achieving the best balance between policy value and constraint satisfaction rate in experiments.
Recently, self-learning methods based on user satisfaction metrics and contextual bandits have shown promising results to enable consistent improvements in conversational AI systems. However, directly targeting such metrics by off-policy bandit learning objectives often increases the risk of making abrupt policy changes that break the current user experience. In this study, we introduce a scalable framework for supporting fine-grained exploration targets for individual domains via user-defined constraints. For example, we may want to ensure fewer policy deviations in business-critical domains such as shopping, while allocating more exploration budget to domains such as music. Furthermore, we present a novel meta-gradient learning approach that is scalable and practical to address this problem. The proposed method adjusts constraint violation penalty terms adaptively through a meta objective that encourages balanced constraint satisfaction across domains. We conduct extensive experiments using data from a real-world conversational AI on a set of realistic constraint benchmarks. Based on the experimental results, we demonstrate that the proposed approach is capable of achieving the best balance between the policy value and constraint satisfaction rate.