  In this paper, we propose a deep learning based approach to design online
power control policies for large EH networks, which are often intractable
stochastic control problems. In the proposed approach, for a given EH network,
the optimal online power control rule is learned by training a deep neural
network (DNN), using the solution of offline policy design problem. Under the
proposed scheme, in a given time slot, the transmit power is obtained by
feeding the current system state to the trained DNN. Our results illustrate
that the DNN based online power control scheme outperforms a Markov decision
process based policy. In general, the proposed deep learning based approach can
be used to find solutions to large intractable stochastic control problems.
