Load balancing has become a key technique to handle the increasing traffic demand and improve the user experience. It evenly distributes the traffic across network resources by offloading users from overloaded base stations or channels to less crowded ones. Load balancing is a multi-objective optimization problem involving the automatic adjustment of several parameters to simultaneously maximize multiple network performance indicators. However, the existing methods mostly rely on single-objective approaches which lead to sub-optimal solutions. In this paper, we introduce the first multi-objective reinforcement learning (MORL) framework for load balancing. Specifically, we propose a solution based on meta-reinforcement learning (meta-RL) to learn a general policy capable of quickly adapting to new trade-offs between the objectives. We further enhance the generalization of our proposed solution using policy distillation techniques. To showcase the effectiveness of our framework, experiments are conducted based on real-world traffic scenarios. Our results show that our load balancing framework can (i) significantly outperform the existing rule-based and single-objective solutions, (ii) compute better Pareto front approximations compared to MORL baselines, and (iii) quickly adapt to new objective trade-offs.

中文翻译：

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多频带下行蜂窝网络的多目标负载平衡：一种元强化学习方法

负载均衡已成为应对日益增长的流量需求和改善用户体验的关键技术。它通过将用户从过载的基站或信道卸载到不那么拥挤的信道，在网络资源上均匀分配流量。负载均衡是一个多目标优化问题，涉及自动调整多个参数以同时最大化多个网络性能指标。然而，现有的方法大多依赖于导致次优解决方案的单目标方法。在本文中，我们介绍了第一个用于负载平衡的多目标强化学习 (MORL) 框架。具体来说，我们提出了一种基于元强化学习 (meta-RL) 的解决方案，以学习能够快速适应目标之间新权衡的一般策略。我们使用策略蒸馏技术进一步增强了我们提出的解决方案的泛化性。为了展示我们框架的有效性，我们根据现实世界的交通场景进行了实验。我们的结果表明，我们的负载平衡框架可以 (i) 显着优于现有的基于规则和单目标解决方案，(ii) 与 MORL 基线相比，计算更好的帕累托前沿近似值，以及 (iii) 快速适应新的目标权衡.