Multi-objective Optimization of Notifications Using Offline Reinforcement Learning

In this paper, Mobile notification systems play a major role in a variety of applications we focus our discussion on a near-real-time notification system, to communicate, send alerts and reminders to the users to which can process both near-real-time and offline notifications and inform them about news, events or messages. In this paper, we formulate make decisions in a stream fashion in near-real-time. An example of the near-real-time notification decision problem as a Markov such a distributed near-real-time notification system can be found Decision Process where we optimize for multiple objectives in the in [7]. Note that a near-real-time notification system can process rewards. We propose an end-to-end offline reinforcement learning offline notifications and spread them out over time, for example framework to optimize sequential notification decisions. We using a notification spacing queuing system introduced in [35]. On address the challenge of offline learning using a Double Deep Q-the other hand, a system designed solely for offline notifications network method based on Conservative Q-learning that mitigates may not be able to process near-real-time notifications. the distributional shift problem and Q-value overestimation. We There are a few characteristics of the notification system that illustrate our fully-deployed system and demonstrate the performance make them suitable applications for reinforcement learning (RL).