  6G will move mobile networks towards increasing levels of complexity. To deal
with this complexity, optimization of network parameters is key to ensure high
performance and timely adaptivity to dynamic network environments. The
optimization of the antenna tilt provides a practical and cost-efficient method
to improve coverage and capacity in the network. Previous methods based on
Reinforcement Learning (RL) have shown great promise for tilt optimization by
learning adaptive policies outperforming traditional tilt optimization methods.
However, most existing RL methods are based on single-cell features
representation, which fails to fully characterize the agent state, resulting in
suboptimal performance. Also, most of such methods lack scalability, due to
state-action explosion, and generalization ability. In this paper, we propose a
Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on
a graph attention mechanism to select relevant neighbors information, improve
the agent state representation, and update the tilt control policy based on a
history of observations using a Deep Q-Network (DQN). We show that GAQ
efficiently captures important network information and outperforms standard DQN
with local information by a large margin. In addition, we demonstrate its
ability to generalize to network deployments of different sizes and densities.
