Value Penalized Q-Learning for Recommender Systems
This addresses distributional shift issues in offline RL for recommender systems, offering a practical plugin to enhance long-term customer satisfaction, though it appears incremental as it builds on existing uncertainty-based methods.
The paper tackles the challenge of applying offline reinforcement learning to recommender systems with high-dimensional action spaces and non-stationary dynamics by proposing Value Penalized Q-learning (VPQ), which penalizes unstable Q-values using uncertainty-aware weights derived from ensemble variances, and experiments on two real-world datasets show it can serve as a gain plugin for existing models.
Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improving customers' long-term satisfaction. A key approach to this goal is offline RL, which aims to learn policies from logged data. However, the high-dimensional action space and the non-stationary dynamics in commercial RS intensify distributional shift issues, making it challenging to apply offline RL methods to RS. To alleviate the action distribution shift problem in extracting RL policy from static trajectories, we propose Value Penalized Q-learning (VPQ), an uncertainty-based offline RL algorithm. It penalizes the unstable Q-values in the regression target by uncertainty-aware weights, without the need to estimate the behavior policy, suitable for RS with a large number of items. We derive the penalty weights from the variances across an ensemble of Q-functions. To alleviate distributional shift issues at test time, we further introduce the critic framework to integrate the proposed method with classic RS models. Extensive experiments conducted on two real-world datasets show that the proposed method could serve as a gain plugin for existing RS models.