  In this letter, we study the energy efficiency (EE) optimisation of unmanned
aerial vehicles (UAVs) providing wireless coverage to static and mobile ground
users. Recent multi-agent reinforcement learning approaches optimise the
system's EE using a 2D trajectory design, neglecting interference from nearby
UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's
3D trajectory, number of connected users, and the energy consumed, while
accounting for interference. Thus, we propose a cooperative Multi-Agent
Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach
outperforms existing baselines in terms of EE by as much as 55 -- 80%.
