  Recent years witnessed a remarkable increase in the availability of data and
computing resources in communication networks. This contributed to the rise of
data-driven over model-driven algorithms for network automation. This paper
investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement
Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas
tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT
data, electromagnetic simulations, and network Key Performance indicators
(KPIs) to define a simulated network environment for the training of a Deep
Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN
formulation to improve the agent's sample efficiency, stability, and
performance. In particular, a custom exploration policy is designed to
introduce soft constraints at training time. Results show that the proposed
algorithm outperforms baseline approaches like DQN and best-fist search in
terms of long-term reward and sample efficiency. Our results indicate that
MDT-driven approaches constitute a valuable tool for autonomous coverage and
capacity optimization of mobile radio networks.
