This paper proposes a method for wireless network optimization applicable to tuning cell parameters that impact the performance of the adjusted cell and the surrounding neighboring cells. The method relies on multiple reinforcement learning agents that share a common policy and include information from neighboring cells in the state and reward. In order not to impair network performance during the first steps of learning, agents are pre-trained during an earlier phase of offline learning, in which an initial policy is obtained using feedback from a static network simulator and considering a wide variety of scenarios. Finally, agents can wisely tune the cell parameters of a test network by suggesting small incremental changes to slowly steer the network toward an optimal configuration. Agents propose optimal changes using the experience gained with the simulator in the pre-training phase, but also continue to learn from current network readings after each change. The results show how the proposed approach significantly improves the performance gains already provided by expert system-based methods when applied to remote antenna tilt optimization. Additional gains are also seen when comparing the proposed approach with a similar method in which the state and reward do not include information from neighboring cells.

翻译：本文提出一种适用于无线网络优化的方法，用于调整影响目标小区及周边相邻小区性能的蜂窝参数。该方法依赖于多个共享共同策略的强化学习智能体，并在状态与奖励中纳入相邻小区的信息。为避免学习初始阶段损害网络性能，智能体在离线学习的早期阶段进行预训练，通过静态网络模拟器的反馈并考虑多种场景来获取初始策略。最终，智能体能够通过建议微小的增量变更，将测试网络逐步引导至最优配置，从而明智地调整其小区参数。智能体利用预训练阶段从模拟器获得的经验提出最优变更方案，同时每次变更后持续从当前网络读数中学习。结果表明，与基于专家系统的方法相比，所提方法在远程天线下倾角优化中显著提升了性能增益。此外，与未在状态和奖励中纳入相邻小区信息的类似方法相比，所提方案还展现了额外增益。