# Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization

arXiv: https://arxiv.org/abs/2203.03227 · HTML: https://mlsanity.com/paper/2203.03227/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Deep Energy Estimator Networks](https://mlsanity.com/paper/1805.08306/) (2018)
- [Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration](https://mlsanity.com/paper/2006.12749/) (2020)

## Built on by (most cited first, 2 of 2)

- [Offline Reinforcement Learning for Mobility Robustness Optimization](https://mlsanity.com/paper/2506.22793/) (2025)
- [Compositional Learning for Modular Multi-Agent Self-Organizing Networks](https://mlsanity.com/paper/2506.02616/) (2025)

## Abstract (arXiv)

The legacy mobility robustness optimization (MRO) in self-organizing networks aims at improving handover performance by optimizing cell-specific handover parameters. However, such solutions cannot satisfy the needs of next-generation network with network slicing, because it only guarantees the received signal strength but not the per-slice service quality. To provide the truly seamless mobility service, we propose a deep reinforcement learning-based slice-aware mobility robustness optimization (SAMRO) approach, which improves handover performance with per-slice service assurance by optimizing slice-specific handover parameters. Moreover, to allow safe and sample efficient online training, we develop a two-step transfer learning scheme: 1) regularized offline reinforcement learning, and 2) effective online fine-tuning with mixed experience replay. System-level simulations show that compared against the legacy MRO algorithms, SAMRO significantly improves slice-aware service continuation while optimizing the handover performance.