mlsanityData to 7 Oct 2026

Multi-objective Optimization of Notifications Using Offline Reinforcement Learning

arXiv 2207.03029 ↗First posted 7 Jul 2022cs.LG stat.MLcited by 4

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 20 of its 38 references that are in the map. First posted on arXiv 7 Jul 2022.

Builds on

20 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Offline Reinforcement Learning for Mobile Notifications4 Feb

Other references · 19

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  5. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  6. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  7. 2019Model-Based Reinforcement Learning for Ataricited by 495
  8. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 285
  9. 2019Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialogcited by 283
  10. 2019RoboNet: Large-Scale Multi-Robot Learningcited by 268
  11. 2020Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learningcited by 213
  12. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 181
7 more
  1. 2018Deep Reinforcement Learning and the Deadly Triadcited by 170
  2. 2019Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Samplingcited by 118
  3. 2019Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systemscited by 90
  4. 2016Multi-Objective Deep Reinforcement Learningcited by 81
  5. 2019Diagnosing Bottlenecks in Deep Q-learning Algorithmscited by 73
  6. 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
  7. 2019Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodologycited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systemscited by 2
  2. 2024TIM: Temporal Interaction Model in Notification Systemcited by 1
  3. 2026Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Studycited by 0
  4. 2026A Self-Triggered Agentic Push Recommendation Systemcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative Sequential Notification Optimization via Multi-Objective Decision Transformers
  2. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control
  3. 2022BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion
  4. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
  5. 2020EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
  6. 2022Mildly Conservative Q-Learning for Offline Reinforcement Learning
  7. 2025Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
  8. 2022Settling the Communication Complexity for Distributed Offline Reinforcement Learning