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.

本文提出了一种基于增强学习的无线网络优化方法，该方法使用多个智能体，可以通过调整单元参数来优化无线网络的表现，从而显着提高网络性能。