  In this paper, we propose reconfigurable intelligent surface (RIS)-assisted
unmanned aerial vehicles (UAVs) networks that can utilise both advantages of
UAV's agility and RIS's reflection for enhancing the network's performance. To
aim at maximising the energy efficiency (EE) of the considered networks, we
jointly optimise the power allocation of the UAVs and the phase-shift matrix of
the RIS. A deep reinforcement learning (DRL) approach is proposed for solving
the continuous optimisation problem with time-varying channels in a centralised
fashion. Moreover, a parallel learning approach is also proposed for reducing
the information transmission requirement of the centralised approach. Numerical
results show a significant improvement of our proposed schemes compared with
the conventional approaches in terms of EE, flexibility, and processing time.
Our proposed DRL methods for RIS-assisted UAV networks can be used for
real-time applications due to their capability of instant decision-making and
handling the time-varying channel with the dynamic environmental setting.
