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arXiv · 2207.03029

Multi-objective Optimization of Notifications Using Offline Reinforcement Learning

Prakruthi Prabhakar·Yiping Yuan·Guangyu Yang·Wensheng Sun·Ajith Muralidharan
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Abstract

Mobile notification systems play a major role in a variety of applications to communicate, send alerts and reminders to the users to inform them about news, events or messages. In this paper, we formulate the near-real-time notification decision problem as a Markov Decision Process where we optimize for multiple objectives in the rewards. We propose an end-to-end offline reinforcement learning framework to optimize sequential notification decisions. We address the challenge of offline learning using a Double Deep Q-network method based on Conservative Q-learning that mitigates the distributional shift problem and Q-value overestimation. We illustrate our fully-deployed system and demonstrate the performance and benefits of the proposed approach through both offline and online experiments.

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Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun, Ajith Muralidharan. 2022-07-07. Multi-objective Optimization of Notifications Using Offline Reinforcement Learning. https://arxiv.org/abs/2207.03029

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