Abstract
We consider a multicast scheme recently proposed for a wireless downlink [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. In the longer version of the paper [2], we also demonstrate that our learning algorithm can be modified to allow the optimal control to track the time varying system statistics.