  In this paper, we study the resource allocation problem for an intelligent
reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization
framework is formulated by jointly optimizing subcarrier allocation, base
station transmit beamforming and IRS phase shift. Considering the continuous
and discrete hybrid action space characteristics of the optimization variables,
we propose an efficient resource allocation algorithm combining multiple deep Q
networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this
issue. In our algorithm, MDQN are employed to solve the problem of large
discrete action space, while DDPG is introduced to tackle the continuous action
allocation. Compared with the traditional approaches, our proposed MDQN-DDPG
based algorithm has the advantage of continuous behavior improvement through
learning from the environment. Simulation results demonstrate superior
performance of our design in terms of system sum rate compared with the
benchmark schemes.
