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

Deep Reinforcement Learning Based Power control for Wireless Multicast Systems