  In this paper, we propose a deep reinforcement learning (DRL) approach for
solving the optimisation problem of the network's sum-rate in device-to-device
(D2D) communications supported by an intelligent reflecting surface (IRS). The
IRS is deployed to mitigate the interference and enhance the signal between the
D2D transmitter and the associated D2D receiver. Our objective is to jointly
optimise the transmit power at the D2D transmitter and the phase shift matrix
at the IRS to maximise the network sum-rate. We formulate a Markov decision
process and then propose the proximal policy optimisation for solving the
maximisation game. Simulation results show impressive performance in terms of
the achievable rate and processing time.
