  Deep reinforcement learning offers a model-free alternative to supervised
deep learning and classical optimization for solving the transmit power control
problem in wireless networks. The multi-agent deep reinforcement learning
approach considers each transmitter as an individual learning agent that
determines its transmit power level by observing the local wireless
environment. Following a certain policy, these agents learn to collaboratively
maximize a global objective, e.g., a sum-rate utility function. This
multi-agent scheme is easily scalable and practically applicable to large-scale
cellular networks. In this work, we present a distributively executed
continuous power control algorithm with the help of deep actor-critic learning,
and more specifically, by adapting deep deterministic policy gradient.
Furthermore, we integrate the proposed power control algorithm to a
time-slotted system where devices are mobile and channel conditions change
rapidly. We demonstrate the functionality of the proposed algorithm using
simulation results.
