# Offline Reinforcement Learning for Mobility Robustness Optimization

arXiv: https://arxiv.org/abs/2506.22793 · HTML: https://mlsanity.com/paper/2506.22793/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Offline RL and imitation learning from data / [Offline multi-agent RL](https://mlsanity.com/offlinerl/offline-multi-agent-rl/)

## Builds on (most cited first, 6 of 6)

- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [When should we prefer Decision Transformers for Offline Reinforcement Learning?](https://mlsanity.com/paper/2305.14550/) (2023)
- [Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation](https://mlsanity.com/paper/2410.23031/) (2024)
- [Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization](https://mlsanity.com/paper/2203.03227/) (2022)
- [Towards Mobility Management with Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2301.10635/) (2023)

## Built on by (most cited first, 1 of 1)

- [Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control](https://mlsanity.com/paper/2603.03932/) (2026)

## 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.