mlsanityData to 14 Oct 2026

Offline Reinforcement Learning for Mobility Robustness Optimization

arXiv 2506.22793 ↗First posted 28 Jun 2025cs.NI cs.AI cs.PFcited by 1

On the map: Offline RL and imitation learning from data → Offline multi-agent RL

Abstract (arXiv)

In this work we revisit the Mobility Robustness Optimisation (MRO) algorithm and study the possibility of learning the optimal Cell Individual Offset tuning using offline Reinforcement Learning. Such methods make use of collected offline datasets to learn the optimal policy, without further exploration. We adapt and apply a sequence-based method called Decision Transformers as well as a value-based method called Conservative Q-Learning to learn the optimal policy for the same target reward as the vanilla rule-based MRO. The same input features related to failures, ping-pongs, and other handover issues are used. Evaluation for realistic New Radio networks with 3500 MHz carrier frequency on a traffic mix including diverse user service types and a specific tunable cell-pair shows that offline-RL methods outperform rule-based MRO, offering up to 7% improvement. Furthermore, offline-RL can be trained for diverse objective functions using the same available dataset, thus offering operational flexibility compared to rule-based methods.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Offline multi-agent RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 11 references that are in the map. First posted on arXiv 28 Jun 2025.

Builds on

6 of 11 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation30 Oct

Other references · 5

  1. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  2. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  3. 2023When should we prefer Decision Transformers for Offline Reinforcement Learning?cited by 20
  4. 2022Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimizationcited by 2
  5. 2023Towards Mobility Management with Multi-Objective Bayesian Optimizationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Controlcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
  2. 2025Offline and Distributional Reinforcement Learning for Wireless Communications
  3. 2023Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
  4. 2026Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
  5. 2026Offline Multi-Agent Reinforcement Learning for 6G Communications: Fundamentals, Applications and Future Directions
  6. 2025Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning
  7. 2024Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning
  8. 2024Offline and Distributional Reinforcement Learning for Radio Resource Management