# Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios

arXiv: https://arxiv.org/abs/2303.16685 · HTML: https://mlsanity.com/paper/2303.16685/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2111.08067/) (2021)
- [Load Balancing for Ultra-Dense Networks: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/1906.00767/) (2019)
- [Meta-Reinforcement Learning for Building Energy Management System](https://mlsanity.com/paper/2210.12590/) (2022)
- [Handover adaptation for dynamic load balancing in 3gpp long term evolution systems](https://mlsanity.com/paper/1307.1212/) (2013)

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

- [Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization](https://mlsanity.com/paper/2312.03277/) (2023)

## Abstract (arXiv)

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving increasing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.