  We consider a multicast scheme recently proposed for a wireless downlink in
[1]. It was shown earlier that power control can significantly improve its
performance. However for this system, obtaining optimal power control is
intractable because of a very large state space. Therefore in this paper we use
deep reinforcement learning where we use function approximation of the
Q-function via a deep neural network. We show that optimal power control can be
learnt for reasonably large systems via this approach. The average power
constraint is ensured via a Lagrange multiplier, which is also learnt. Finally,
we demonstrate that a slight modification of the learning algorithm allows the
optimal control to track the time varying system statistics.
